CN101726255B - Method for extracting interesting buildings from three-dimensional laser point cloud data - Google Patents
Method for extracting interesting buildings from three-dimensional laser point cloud data Download PDFInfo
- Publication number
- CN101726255B CN101726255B CN2008102249102A CN200810224910A CN101726255B CN 101726255 B CN101726255 B CN 101726255B CN 2008102249102 A CN2008102249102 A CN 2008102249102A CN 200810224910 A CN200810224910 A CN 200810224910A CN 101726255 B CN101726255 B CN 101726255B
- Authority
- CN
- China
- Prior art keywords
- point
- target
- buildings
- line segment
- cloud data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000000605 extraction Methods 0.000 claims abstract description 8
- 239000000284 extract Substances 0.000 claims description 20
- 239000012141 concentrate Substances 0.000 claims description 15
- 238000012217 deletion Methods 0.000 claims description 9
- 230000037430 deletion Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 4
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims description 3
- 244000062793 Sorghum vulgare Species 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000002386 leaching Methods 0.000 claims description 3
- 235000019713 millet Nutrition 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000004321 preservation Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 abstract description 2
- 238000005192 partition Methods 0.000 abstract 2
- 238000012545 processing Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
Images
Landscapes
- Processing Or Creating Images (AREA)
Abstract
The invention discloses a method for extracting interesting buildings from three-dimensional laser point cloud data. The method comprises the following steps of: fixing building candidate points from filtered non-ground points; clustering the fixed candidate points by taking point-to-point distances as constraint to complete partition of single building target; respectively extracting marginal points of each building target; regularize the margin of each building object and recording target information to make into a target look-up table; and searching out all objects according with the characteristics of a target a user interests from the target look-up table and supplying to the user. The method can assist the user to search an interesting target by the target partition of the point cloud data organized by using a kd tree after filtering, the extraction of marginal points, the regularization of the margin and the provision of the extracted characteristic information of the building.
Description
Technical field
The present invention relates to buildings Point Cloud Processing technical field, relate in particular to a kind of method of from three-dimensional laser point cloud data, extracting interesting buildings.
Background technology
Buildings is a most important research object in the city, and many practical applications are all relevant with buildings, such as city planning, public safety research, environmental monitoring research, wireless telecommunications, disaster assessment etc.The detection of buildings, to extract with reconstruction also be one of the main research topic in field such as photogrammetric, remote sensing.Although photogrammetric survey method remains the effective means of the extensive city of present extraction building, but also there are many difficulties in the extraction based on image, the noise of image, illumination condition, shade, blocks and the complicacy of scene makes the detection of buildings become quite complicated.Active sensor, the city data that obtain such as airborne LiDAR are not subjected to the influence of illumination, shade, and can directly obtain the elevation information of object, and rebuilding for buildings provides desirable data source.
Laser acquisition and range finding (Light Detection and Ranging, LiDAR) technology, also claim laser scanning (Laser Scanning) technology, because of it has the characteristics of the information of obtaining object surface that laser arrives quickly and accurately, become the important tool of obtaining extensive 3D landforms in recent years.The cycle of obtaining of LiDAR data is short, automaticity is high, yet existing most of LiDAR point cloud Processing Algorithm are used for quality control in practice and the manual time has occupied sizable ratio in whole data processing time.Therefore, automatic, efficient, sane LiDAR point cloud classification and the target extraction algorithm of design has important researching value.
The edge wheel profile of buildings is the key character of building model, and it can be directly used in the polygon model that buildings extracts, makes up buildings, also can be used for the auxiliary buildings edge accurately that extracts from aviation image.Before extracting the buildings edge, three dimensional point cloud need be carried out the filtering classification, promptly determine earlier can improve subsequent treatment speed like this to obtain better effect in the buildings region.
Vosselman carries out trigonometric ratio to the construction zone that original LiDAR point cloud is described, and the polygon that its borderline leg-of-mutton limit is constituted is as the initial description of contour of building.This algorithm hypothesis contour of building is made up of straight-line segment, and a principal direction is arranged, these straight-line segments or parallel or perpendicular to buildings principal direction, thus the refinement that obtains contour of building approaches.Huber projects to detected construction zone with the edge line that extracts also refinement in the 2D image, then each building buildings is carried out the edge cluster, thereby obtains the profile information of buildings.Lai Xudong converts the range data of airborne laser radar to the distance gray scale image, uses classical edge detection operator that it is handled.Jiang Jingjue directly extracts edge line from original LiDAR point cloud, discrete space 3D point is coupled together, and utilizes minimum spanning tree to generate the contour of building vector of describing with the form of broken line from the buildings marginal point.
Summary of the invention
(1) technical matters that will solve
In view of this, at cloud data measuring accuracy height, the characteristics of target three-dimensional coordinate can directly be provided, fundamental purpose of the present invention is to provide a kind of method of directly extracting interesting buildings from three-dimensional laser point cloud data, from three-dimensional laser point cloud data, to extract buildings automatically, efficiently, make things convenient for the user to find interested target fast, and be that follow-up buildings reconstruction is prepared.
(2) technical scheme
For achieving the above object, the technical solution used in the present invention is as follows:
A kind of method of from three-dimensional laser point cloud data, extracting interesting buildings, this method comprises:
From filtered non-ground point, determine the buildings candidate point;
From serving as that constraint is carried out cluster to the candidate point of determining, finish cutting apart of single building target with dot spacing;
Extract the marginal point of each building target respectively;
Edge to each buildings object carries out regularization, and record object information is made the target look-up table; And
From the target look-up table, search out the object that all meet user's interesting target characteristics, offer the user.
In the such scheme, in the described step of from filtered non-ground point, determining the buildings candidate point, be that the point that will meet following condition is defined as candidate point:
1), overhead apart from the point that meets building characteristic; And
2), for removing the interference of non-building object point, a depth image that generates based on a cloud height value is carried out the analysis of elevation texture value, texture value is relatively low or belong to the point of the texture value outburst area of fritter.
In the such scheme, described with dot spacing from serving as that constraint is carried out cluster to the candidate point of determining, finish the step of cutting apart of single building target, specifically comprise:
Utilize the kd tree to organize cloud data, from being constraint, the candidate point of determining is carried out cluster, finish cutting apart of single building target with dot spacing; This cluster and cutting procedure are specific as follows:
A), use the kd tree of setting up to organize cloud data based on standard rule cleave plane;
B), concentrate from candidate point and to take out 1 p at random, put into a new point set, and this point set be labeled as A;
C), search meets the neighborhood point of distance restraint apart from some p, it is concentrated from candidate point takes out and put into point set A;
D), to emerging point among the point set A, concentrate search one by one to meet the neighborhood point of distance restraint at candidate point, the point that searches out is all put into point set A;
E), repeating step d) in point set A, there is not new point to occur;
F) if the number of point set A mid point surpasses threshold value, then point set A is designated as a class, otherwise directly these points is concentrated deletion from candidate point; And
G),, be empty repeating step b) up to candidate's point set to step f).
In the such scheme, the described step of extracting the marginal point of each building target respectively specifically comprises:
I, tentatively identify marginal point and add the edge point set, criterion of identification is: when a p was positioned at edge, the coverage that its k neighbour is ordered was less than half zone around the p, and then whether judging point p is marginal point in view of the above; And
If the height value of minimum point does not have tangible step to change in the marginal point that II identifies and its local horizontal neighborhood, then should concentrate deletion from marginal point by point.
In view of the analyzing spot on the skyscraper side can greatly influence the effect that marginal point extracts, further adopt following step to optimize extraction: for each point in the object, with this point is that a cylinder is done at the center, the radius of cylinder bottom surface is got this distance of putting neighbor point, and the height of cylinder can suitably be chosen according to a size of cloud density; According to ", can have more point in the cylinder of side point " these characteristics, distinguish side point and roof millet cake then with respect to the point on the deck; But this will think side point to the point on side and the deck intersection, so need to finish following treatment step in the leaching process: i), will be labeled as A through the point set that step I and Step II obtain
1Ii), isolate roof point and side point, respectively this roof point and side point are carried out step I and the identification of Step II marginal point, obtain being labeled as A with said method
2And A
3Point set; And iii) according to (A
1∩ A
2) ∪ (A
3-A
1) optimization of finishing marginal point extracts.
In the such scheme, described edge to each buildings object carries out regularization, and record object information is made the step of target look-up table, specifically comprises:
Line segmentization carry out in edge to each buildings object;
The line segment that obtains is adjusted and made up;
After finishing the adjustment and combination of line segment,, then carry out the match of arc if having arc in this object outline; And
The object output feature.
In the such scheme, described the edge of each buildings object is carried out the step of line segmentization, specifically comprises:
A), concentrate picked at random 2 points, form a line segment at marginal point;
B), calculate the distance that other put this line segment place straight line, if less than certain threshold value, think that then this point is on line segment;
C), successively calculate the dot spacing on the line segment, if spacing surpasses certain threshold value, then the line segment that the spacing place is obtained is divided into two sections;
D) if the point on the line segment that step c) obtains is abundant, then fit to new line segment equation and preservation according to points all on the line segment, concentrate these points of deletion from marginal point simultaneously, otherwise, execution in step (e); And
E), to the some repeated execution of steps of remainder a) to step d), till can not find qualified line segment.
In the such scheme, the described step that the line segment that obtains is adjusted and made up specifically comprises: suppose that straight line can be expressed as A
iX+B
iY+C=0, wherein A
iA
i+ B
iB
i=1; Article two, the angle α value between the straight line is [0, pi], can be determined by two formula: i) A
iA
j+ B
iB
j=cos α; Ii) A
iB
j-B
iA
j=sin α; And get an angle threshold β, if the angle of two adjacent segments and 90 degree differences then think vertical in ± β degree; If with 0 degree or 180 degree differences in ± β degree, then think coincidence;
After the described adjustment of finishing line segment and the combination,, then carry out the step of the match of arc, specifically comprise: remaining point is carried out apart from cluster, draw and have several sections arcs if having arc in this object outline; For each segmental arc, adjacent point is connected in turn, to the sequence that finishes to obtain one group of point, fit to level and smooth camber line from initial with least square circle or cubic spline method;
The step of described object output feature, specifically comprise: after finishing above-mentioned regularization process, the characteristics of objects that obtains comprises angle between end face length of side number, each length of side length, each length of side, and several arcs of marginal existence, with the position of these features and object, record highly in the lump, make the target look-up table and be used for follow-up interesting target and search.
In the such scheme, describedly list the object that all meet user's interesting target characteristics, offer user's step from the target look-up table, specifically comprise: according to the characteristics of user's interesting target, from the target look-up table, inquire about, list all qualified objects, offer the user.
(3) beneficial effect
1, this method of directly from three-dimensional laser point cloud data, extracting interesting buildings provided by the invention, based on filtered buildings cloud data, directly the three dimensional point cloud that utilizes kd tree tissue is carried out that target is cut apart, marginal point extracts, regular edgesization, and the building feature that extracts information is provided, search interesting target fast for the user.
2, this method of directly from three-dimensional laser point cloud data, extracting interesting buildings provided by the invention, there is not a cloud interpolation error, be not subjected to the influence of the size or the shape of buildings, can generate and the edge wheel profile original point cloud data tight connecting, after the regularization, for the extraction of interesting buildings target, the reconstruction of building model provide relevant information.
3, this method of directly extracting interesting buildings from three-dimensional laser point cloud data provided by the invention is used kd to set and is organized cloud data, still can keep processing speed faster when the extensive point of processing cloud.
4, this method of directly from three-dimensional laser point cloud data, extracting interesting buildings provided by the invention, directly from three-dimensional point cloud, extract marginal point, obtain the profile information with original point cloud tight connecting, stand good there being air strips situations overlapping or a large amount of side point in the data.
5, this method of directly from three-dimensional laser point cloud data, extracting interesting buildings provided by the invention, only use cloud data just can search the interesting buildings target fast, can offer the user and comprise angle between end face length of side number, each length of side length, each length of side, several arcs of marginal existence, and target signature such as position of object, height.
Description of drawings
Fig. 1 is the method flow diagram that extracts interesting buildings from three-dimensional laser point cloud data provided by the invention.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in more detail.
As shown in Figure 1, Fig. 1 is the method flow diagram that extracts interesting buildings from three-dimensional laser point cloud data provided by the invention, and this method comprises:
Step 101: from filtered non-ground point, determine the buildings candidate point;
Step 102: from serving as that constraint is carried out cluster to the candidate point of determining, finish cutting apart of single building target with dot spacing;
Step 103: the marginal point that extracts each building target respectively;
Step 104: the edge to each buildings object carries out regularization, and record object information is made the target look-up table; And
Step 105: from the target look-up table, search out the object that all meet user's interesting target characteristics, offer the user.
Determining the buildings candidate point described in the above-mentioned steps 101 from filtered non-ground point, is that the point that will meet following condition is defined as candidate point:
1), overhead apart from the point that meets building characteristic; And
2), for removing the interference of non-building object point, a depth image that generates based on a cloud height value is carried out the analysis of elevation texture value, texture value is relatively low or belong to the point of the texture value outburst area of fritter.
Described in the above-mentioned steps 102 with dot spacing from serving as that constraint is carried out cluster to the candidate point of determining, finish cutting apart of single building target, specifically comprise:
Utilize the kd tree to organize cloud data, from being constraint, the candidate point of determining is carried out cluster, finish cutting apart of single building target with dot spacing.This cluster and cutting procedure are specific as follows:
A), use the kd tree of setting up to organize cloud data based on standard rule cleave plane;
B), concentrate from candidate point and to take out 1 p at random, put into a new point set, and this point set be labeled as A;
C), search meets the neighborhood point of distance restraint apart from some p, it is concentrated from candidate point takes out and put into point set A;
D), to emerging point among the point set A, concentrate search one by one to meet the neighborhood point of distance restraint at candidate point, the point that searches out is all put into point set A;
E), repeating step d) in point set A, there is not new point to occur;
F) if the number of point set A mid point surpasses threshold value, then point set A is designated as a class, otherwise directly these points is concentrated deletion from candidate point; And
G),, be empty repeating step b) up to candidate's point set to step f).
Extract the marginal point of each building target described in the above-mentioned steps 103 respectively, specifically comprise:
I, tentatively identify marginal point and add the edge point set, criterion of identification is: when a p was positioned at edge, the coverage that its k neighbour is ordered was less than half zone around the p, and then whether judging point p is marginal point in view of the above; And
If the height value of minimum point does not have tangible step to change in the marginal point that II identifies and its local horizontal neighborhood, then should concentrate deletion from marginal point by point.
In view of the analyzing spot on the skyscraper side can greatly influence the effect that marginal point extracts, this method can further adopt following step to optimize extraction.For each point in the object, be that a cylinder is done at the center with this point, the radius of cylinder bottom surface is got this distance of putting neighbor point, and the height of cylinder can suitably be chosen according to a size of cloud density; Then according to " with respect to the point on the deck; can there be more point in the cylinder of side point " these characteristics, distinguish side point and roof millet cake, but this will think side point to the point on side and the deck intersection, so need to finish following treatment step in the leaching process: i), will be labeled as A through the point set that step I and Step II obtain
1Ii), isolate roof point and side point, respectively this roof point and side point are carried out step I and the identification of Step II marginal point, obtain being labeled as A with said method
2And A
3Point set; And iii) according to (A
1∩ A
2) ∪ (A
3-A
1) optimization of finishing marginal point extracts.
Edge to each buildings object described in the above-mentioned steps 104 carries out regularization, and record object information is made the target look-up table, specifically comprises:
Step 1041: line segmentization carry out in the edge to each buildings object; Specifically comprise:
A), concentrate picked at random 2 points, form a line segment at marginal point;
B), calculate the distance that other put this line segment place straight line, if less than certain threshold value, think that then this point is on line segment;
C), successively calculate the dot spacing on the line segment, if spacing surpasses certain threshold value, then the line segment that the spacing place is obtained is divided into two sections;
D) if the point on the line segment that step c) obtains is abundant, then fit to new line segment equation and preservation according to points all on the line segment, concentrate these points of deletion from marginal point simultaneously, otherwise, execution in step (e); And
E), to the some repeated execution of steps of remainder a) to step d), till can not find qualified line segment.
Step 1042: the line segment that obtains is adjusted and made up; Specifically comprise: suppose that straight line can be expressed as A
iX+B
iY+C=0, wherein A
iA
i+ B
iB
i=1; Article two, the angle α value between the straight line is [0, pi], can be determined by two formula: i) A
iA
j+ B
iB
j=cos α; Ii) A
iB
j-B
iA
j=sin α; And get an angle threshold β, if the angle of two adjacent segments and 90 degree differences then think vertical in ± β degree; If with 0 degree or 180 degree differences in ± β degree, then think coincidence.
Step 1043: after finishing the adjustment and combination of line segment,, then carry out the match of arc if having arc in this object outline; Specifically comprise: remaining point is carried out apart from cluster, draw and have several sections arcs; For each segmental arc, adjacent point is connected in turn, to the sequence that finishes to obtain one group of point, fit to level and smooth camber line from initial with least square circle or cubic spline method.
Step 1044: object output feature; Specifically comprise: after finishing above-mentioned regularization process, the characteristics of objects that obtains comprises angle between end face length of side number, each length of side length, each length of side, and several arcs of marginal existence, with the position of these features and object, record highly in the lump, make the target look-up table and be used for follow-up interesting target and search.
From the target look-up table, search out the object that all meet user's interesting target characteristics described in the above-mentioned steps 105, offer the user, specifically comprise:, from the target look-up table, inquire about according to the characteristics of user's interesting target, list all qualified objects, offer the user.
Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (7)
1. method of extracting interesting buildings from three-dimensional laser point cloud data is characterized in that this method comprises:
From filtered non-ground point, determine the buildings candidate point;
From serving as that constraint is carried out cluster to the candidate point of determining, finish cutting apart of single building target with dot spacing;
Extract the marginal point of each building target respectively;
Edge to each buildings object carries out regularization, and record object information is made the target look-up table; And
From the target look-up table, search out the object that all meet user's interesting target characteristics, offer the user;
Wherein, described with dot spacing from serving as that constraint is carried out cluster to the candidate point of determining, finish the step of cutting apart of single building target, specifically comprise: utilize the kd tree to organize cloud data, from being constraint, the candidate point of determining is carried out cluster with dot spacing, finish cutting apart of single building target; This cluster and cutting procedure are specific as follows:
A), use the kd tree of setting up to organize cloud data based on standard rule cleave plane;
B), concentrate from candidate point and to take out 1 p at random, put into a new point set, and this point set be labeled as A;
C), search meets the neighborhood point of distance restraint apart from some p, it is concentrated from candidate point takes out and put into point set A;
D), to emerging point among the point set A, concentrate search one by one to meet the neighborhood point of distance restraint at candidate point, the point that searches out is all put into point set A;
E), repeating step d) in point set A, there is not new point to occur;
F) if the number of point set A mid point surpasses threshold value, then point set A is designated as a class, otherwise directly these points is concentrated deletion from candidate point; And
G),, be empty repeating step b) up to candidate's point set to step f).
2. the method for extracting interesting buildings from three-dimensional laser point cloud data according to claim 1 is characterized in that, in the described step of determining the buildings candidate point from filtered non-ground point, is that the point that will meet following condition is defined as candidate point:
1), overhead apart from the point that meets building characteristic; And
2), for removing the interference of non-building object point, a depth image that generates based on a cloud height value is carried out the analysis of elevation texture value, texture value is relatively low or belong to the point of the texture value outburst area of fritter.
3. the method for extracting interesting buildings from three-dimensional laser point cloud data according to claim 1 is characterized in that the described step of extracting the marginal point of each building target respectively specifically comprises:
I, tentatively identify marginal point and add the edge point set, criterion of identification is: when a p was positioned at edge, the coverage that its k neighbour is ordered was less than half zone around the p, and then whether judging point p is marginal point in view of the above; And
If the height value of minimum point does not have tangible step to change in the marginal point that II identifies and its local horizontal neighborhood, then should concentrate deletion from marginal point by point.
4. the method for from three-dimensional laser point cloud data, extracting interesting buildings according to claim 3, it is characterized in that, in view of the analyzing spot on the skyscraper side can greatly influence the effect that marginal point extracts, this method further adopts following step to optimize extraction:
For each point in the object, be that a cylinder is done at the center with this point, the radius of cylinder bottom surface is got this distance of putting neighbor point, and the height of cylinder can suitably be chosen according to a size of cloud density; According to ", can have more point in the cylinder of side point " these characteristics, distinguish side point and roof millet cake then with respect to the point on the deck; But this will think side point to the point on side and the deck intersection, so need to finish following treatment step in the leaching process: i), will be labeled as A through the point set that step I and step II obtain
1Ii), isolate roof point and side point, respectively this roof point and side point are carried out step I and the identification of step II marginal point, obtain being labeled as A with said method
2And A
3Point set; And iii) according to (A
1∩ A
2) ∪ (A
3-A
1) optimization of finishing marginal point extracts.
5. the method for extracting interesting buildings from three-dimensional laser point cloud data according to claim 1 is characterized in that described edge to each buildings object carries out regularization, and record object information is made the step of target look-up table, specifically comprises:
Line segmentization carry out in edge to each buildings object;
The line segment that obtains is adjusted and made up;
After finishing the adjustment and combination of line segment,, then carry out the match of arc if having arc in this object outline; And
The object output feature;
Wherein, the described step that the line segment that obtains is adjusted and made up specifically comprises: suppose that straight line can be expressed as A
iX+B
iY+C=0, wherein A
iA
i+ B
iB
i=1; Article two, the angle α value between the straight line is [0, pi], can be determined by two formula: i) A
iA
j+ B
iB
i=cos α; Ii) A
iB
j-B
iA
j=sin α; And get an angle threshold β, if the angle of two adjacent segments and 90 degree differences then think vertical in ± β degree; If with 0 degree or 180 degree differences in ± β degree, then think coincidence;
After the described adjustment of finishing line segment and the combination,, then carry out the step of the match of arc, specifically comprise: remaining point is carried out apart from cluster, draw and have several sections arcs if having arc in this object outline; For each segmental arc, adjacent point is connected in turn, to the sequence that finishes to obtain one group of point, fit to level and smooth camber line from initial with least square circle or cubic spline method;
The step of described object output feature, specifically comprise: after finishing above-mentioned regularization process, the characteristics of objects that obtains comprises angle between end face length of side number, each length of side length, each length of side, and several arcs of marginal existence, with the position of these features and object, record highly in the lump, make the target look-up table and be used for follow-up interesting target and search.
6. the method for extracting interesting buildings from three-dimensional laser point cloud data according to claim 5 is characterized in that, described the edge of each buildings object is carried out the step of line segmentization, specifically comprises:
A), concentrate picked at random 2 points, form a line segment at marginal point;
B), calculate the distance that other put this line segment place straight line, if less than certain threshold value, think that then this point is on line segment;
C), successively calculate the dot spacing on the line segment, if spacing surpasses certain threshold value, then the line segment that the spacing place is obtained is divided into two sections;
D) if the point on the line segment that step c) obtains is abundant, then fit to new line segment equation and preservation according to points all on the line segment, concentrate these points of deletion from marginal point simultaneously, otherwise, execution in step (e); And
E), to the some repeated execution of steps of remainder a) to step d), till can not find qualified line segment.
7. the method for extracting interesting buildings from three-dimensional laser point cloud data according to claim 1 is characterized in that, describedly lists the object that all meet user's interesting target characteristics from the target look-up table, offers user's step, specifically comprises:
According to the characteristics of user's interesting target, from the target look-up table, inquire about, list all qualified objects, offer the user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008102249102A CN101726255B (en) | 2008-10-24 | 2008-10-24 | Method for extracting interesting buildings from three-dimensional laser point cloud data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008102249102A CN101726255B (en) | 2008-10-24 | 2008-10-24 | Method for extracting interesting buildings from three-dimensional laser point cloud data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101726255A CN101726255A (en) | 2010-06-09 |
| CN101726255B true CN101726255B (en) | 2011-05-04 |
Family
ID=42447499
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2008102249102A Expired - Fee Related CN101726255B (en) | 2008-10-24 | 2008-10-24 | Method for extracting interesting buildings from three-dimensional laser point cloud data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101726255B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103148804A (en) * | 2013-03-04 | 2013-06-12 | 清华大学 | Indoor unknown structure identification method based on laser scanning |
Families Citing this family (51)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101887597B (en) * | 2010-07-06 | 2012-07-04 | 中国科学院深圳先进技术研究院 | Construction three-dimensional model building method and system |
| CN102096072B (en) * | 2011-01-06 | 2013-02-13 | 天津市星际空间地理信息工程有限公司 | Method for automatically measuring urban parts |
| WO2013002280A1 (en) * | 2011-06-29 | 2013-01-03 | Ｎｅｃシステムテクノロジー株式会社 | Device for generating three-dimensional feature data, method for generating three-dimensional feature data, and recording medium on which program for generating three-dimensional feature data is recorded |
| EP2600173A1 (en) * | 2011-11-29 | 2013-06-05 | Hexagon Technology Center GmbH | Method for operating a laser scanner |
| CN103018728B (en) * | 2012-11-22 | 2014-06-18 | 北京航空航天大学 | Laser radar real-time imaging and building characteristic extracting method |
| CN102938066B (en) * | 2012-12-07 | 2015-10-28 | 南京大学 | A kind of based on the polygonal method of multivariate data reconstruction buildings outline |
| CN103268609B (en) * | 2013-05-17 | 2016-04-20 | 清华大学 | A kind of some cloud dividing method of orderly extraction ground |
| CN104346798B (en) * | 2013-08-01 | 2019-01-11 | 深圳力维智联技术有限公司 | Objective contour Polygonal Approximation and its device |
| CN103438824B (en) * | 2013-08-06 | 2016-01-20 | 北京航空航天大学 | A kind of large-scale wallboard class Components Digital quality determining method |
| AU2014306485B2 (en) | 2013-08-16 | 2016-12-08 | Landmark Graphics Corporation | Generating a custom reservoir from multiple compartments representing one or more geological structures |
| CN103714541B (en) * | 2013-12-24 | 2015-07-08 | 华中科技大学 | A Method for Identifying and Locating Buildings Using Mountain Contour Area Constraints |
| CN104048618B (en) * | 2014-06-16 | 2016-09-07 | 民政部国家减灾中心 | A kind of damage building detection method |
| CN104077806B (en) * | 2014-07-10 | 2016-10-05 | 天津中科遥感信息技术有限公司 | Automatic split extracting method based on urban architecture threedimensional model |
| CN104573705B (en) * | 2014-10-13 | 2017-10-13 | 北京建筑大学 | A kind of clustering method of building Point Cloud of Laser Scanner |
| CN104457691B (en) * | 2014-12-15 | 2017-02-01 | 重庆市勘测院 | Method for obtaining elevation information of main building body |
| CN104596415B (en) * | 2014-12-29 | 2017-06-09 | 中国神华能源股份有限公司 | A kind of method and apparatus that heap body lower edge is determined based on laser scanning single line |
| CN104966315B (en) * | 2015-05-18 | 2018-02-27 | 深圳市腾讯计算机系统有限公司 | The treating method and apparatus of threedimensional model |
| CN106327558B (en) * | 2015-07-08 | 2019-11-19 | 深圳市腾讯计算机系统有限公司 | Point cloud facade extracting method and device |
| CN105066903B (en) * | 2015-09-09 | 2018-06-12 | 大族激光科技产业集团股份有限公司 | A kind of 3-d laser measurement system and its measuring method |
| CN105333861B (en) * | 2015-12-02 | 2018-02-06 | 中国测绘科学研究院 | The method and device of shaft tower tilt detection based on laser point cloud |
| CN106131401A (en) * | 2016-06-29 | 2016-11-16 | 深圳市金立通信设备有限公司 | A kind of image pickup method and terminal |
| EP3318890B1 (en) * | 2016-11-02 | 2019-05-01 | Aptiv Technologies Limited | Method to provide a vehicle environment contour polyline from detection data |
| CN106530345B (en) * | 2016-11-07 | 2018-12-25 | 江西理工大学 | A kind of building three-dimensional laser point cloud feature extracting method under same machine Image-aided |
| CN106548479B (en) * | 2016-12-06 | 2019-01-18 | 武汉大学 | A kind of multi-level laser point cloud building boundary rule method |
| CN106918813B (en) * | 2017-03-08 | 2019-04-30 | 浙江大学 | A 3D sonar point cloud image enhancement method based on distance statistics |
| CN107452064B (en) * | 2017-05-23 | 2020-10-13 | 巧夺天宫（深圳）科技有限公司 | Three-dimensional building entity space leveling realization method and device and storage equipment |
| CN107390679B (en) * | 2017-06-13 | 2020-05-05 | 合肥中导机器人科技有限公司 | Storage device and laser navigation forklift |
| CN107784682B (en) * | 2017-09-26 | 2020-07-24 | 厦门大学 | A method for automatic cable extraction and reconstruction based on 3D point cloud data |
| CN108171720A (en) * | 2018-01-08 | 2018-06-15 | 武汉理工大学 | A kind of oblique photograph model object frontier probe method based on geometrical statistic information |
| CN109166149B (en) * | 2018-08-13 | 2021-04-02 | 武汉大学 | Positioning and three-dimensional line frame structure reconstruction method and system integrating binocular camera and IMU |
| CN109614857B (en) * | 2018-10-31 | 2020-09-29 | 百度在线网络技术（北京）有限公司 | Point cloud-based rod identification method, device, equipment and storage medium |
| CN111210500B (en) * | 2018-11-22 | 2023-08-29 | 浙江欣奕华智能科技有限公司 | Three-dimensional point cloud processing method and device |
| CN109903304B (en) * | 2019-02-25 | 2021-03-16 | 武汉大学 | Automatic building contour extraction algorithm based on convolutional neural network and polygon regularization |
| CN109993783B (en) * | 2019-03-25 | 2020-10-27 | 北京航空航天大学 | Roof and side surface optimization reconstruction method for complex three-dimensional building point cloud |
| CN110047133A (en) * | 2019-04-16 | 2019-07-23 | 重庆大学 | A kind of train boundary extraction method towards point cloud data |
| CN110414379A (en) * | 2019-07-10 | 2019-11-05 | 武汉大学 | A Building Extraction Algorithm Combining Elevation Map Gabor Texture Features and LiDAR Point Cloud Features |
| CN110717983B (en) * | 2019-09-07 | 2023-05-02 | 苏州工业园区测绘地理信息有限公司 | Building elevation three-dimensional reconstruction method based on knapsack type three-dimensional laser point cloud data |
| CN110888143B (en) * | 2019-10-30 | 2022-09-13 | 中铁四局集团第五工程有限公司 | Bridge through measurement method based on unmanned aerial vehicle airborne laser radar |
| CN111626096B (en) * | 2020-04-08 | 2023-08-08 | 南京航空航天大学 | A Method for Extracting Interest Points from 3D Point Cloud Data |
| CN111652892A (en) * | 2020-05-02 | 2020-09-11 | 王磊 | Remote sensing image building vector extraction and optimization method based on deep learning |
| CN112700464B (en) * | 2021-01-15 | 2022-03-29 | 腾讯科技（深圳）有限公司 | Map information processing method and device, electronic equipment and storage medium |
| CN112949407B (en) * | 2021-02-02 | 2022-06-14 | 武汉大学 | Remote sensing image building vectorization method based on deep learning and point set optimization |
| CN112945198B (en) * | 2021-02-02 | 2023-01-31 | 贵州电网有限责任公司 | Automatic detection method for inclination of power transmission line iron tower based on laser LIDAR point cloud |
| CN112733971B (en) * | 2021-04-02 | 2021-11-16 | 北京三快在线科技有限公司 | Pose determination method, device and equipment of scanning equipment and storage medium |
| CN114894157A (en) * | 2022-04-13 | 2022-08-12 | 中国能源建设集团江苏省电力设计院有限公司 | Laser point cloud layering-based transmission tower gradient calculation method and system |
| CN114898118A (en) * | 2022-06-02 | 2022-08-12 | 中国能源建设集团江苏省电力设计院有限公司 | Automatic statistical method and system for power transmission line house removal amount based on multi-source point cloud |
| CN115329899A (en) * | 2022-10-12 | 2022-11-11 | 广东电网有限责任公司中山供电局 | Clustering equivalent model construction method, system, equipment and storage medium |
| CN116310081A (en) * | 2022-12-31 | 2023-06-23 | 南京回归建筑环境设计研究院有限公司 | Building model based on point cloud data design and system thereof |
| CN116580048B (en) * | 2023-07-12 | 2023-09-26 | 武汉峰岭科技有限公司 | Method and system for extracting contour line of right-angle house on house inclination model |
| CN117218189A (en) * | 2023-08-17 | 2023-12-12 | 云南昆钢电子信息科技有限公司 | Suspended object detection method and device for laser point cloud-to-depth image |
| CN118799381A (en) * | 2024-02-29 | 2024-10-18 | 中科吉芯(秦皇岛)信息技术有限公司 | Method and system for determining track centerline |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1684105A (en) * | 2004-04-13 | 2005-10-19 | 清华大学 | Automatic registration method for large-scale three-dimensional scene multi-view laser scanning data |
-
2008
- 2008-10-24 CN CN2008102249102A patent/CN101726255B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1684105A (en) * | 2004-04-13 | 2005-10-19 | 清华大学 | Automatic registration method for large-scale three-dimensional scene multi-view laser scanning data |
Non-Patent Citations (3)
| Title |
|---|
| Jiang jingjue et al.,.Filtering of Airborne Lidar Point Clouds for Complex Cityscapes.《Geo-Spatial Information Science》.2008,(第1期),全文. * |
| 王刃 等.用多种策略从机载Lidar 数据中提取建筑脚点.《武汉大学学报 信息科学学版》.2008,第33卷(第7期),全文. |
| 王刃等.用多种策略从机载Lidar 数据中提取建筑脚点.《武汉大学学报 信息科学学版》.2008,第33卷(第7期),全文. * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103148804A (en) * | 2013-03-04 | 2013-06-12 | 清华大学 | Indoor unknown structure identification method based on laser scanning |
| CN103148804B (en) * | 2013-03-04 | 2015-05-20 | 清华大学 | Indoor unknown structure identification method based on laser scanning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101726255A (en) | 2010-06-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101726255B (en) | Method for extracting interesting buildings from three-dimensional laser point cloud data | |
| CN110717983B (en) | Building elevation three-dimensional reconstruction method based on knapsack type three-dimensional laser point cloud data | |
| CN108195377B (en) | Reflector matching algorithm based on triangular perimeter matching | |
| CN106780524B (en) | A 3D point cloud road boundary automatic extraction method | |
| CN109949326B (en) | Building contour line extraction method based on knapsack type three-dimensional laser point cloud data | |
| CN106127153B (en) | The traffic sign recognition methods of Vehicle-borne Laser Scanning point cloud data | |
| Xia et al. | A fast edge extraction method for mobile LiDAR point clouds | |
| CN105866790B (en) | A kind of laser radar obstacle recognition method and system considering lasing intensity | |
| CN109657698B (en) | Magnetic suspension track obstacle detection method based on point cloud | |
| CN106204705B (en) | A kind of 3D point cloud dividing method based on multi-line laser radar | |
| CN107301648B (en) | Redundant point cloud removing method based on overlapping area boundary angle | |
| CN104200212A (en) | Building outer boundary line extraction method based on onboard LiDAR (Light Detection and Ranging) data | |
| CN109144072A (en) | A kind of intelligent robot barrier-avoiding method based on three-dimensional laser | |
| CN104657968B (en) | Automatic vehicle-mounted three-dimensional laser point cloud facade classification and outline extraction method | |
| CN103760569A (en) | Drivable region detection method based on laser radar | |
| CN101604450A (en) | Method of integrating image and LiDAR data to extract building outline | |
| CN104183017A (en) | Ground three-dimensional laser point cloud based method for realizing automatic extraction of geologic body occurrence | |
| CN103605135A (en) | Road feature extracting method based on fracture surface subdivision | |
| CN103727930A (en) | Edge-matching-based relative pose calibration method of laser range finder and camera | |
| CN104880160A (en) | Two-dimensional-laser real-time detection method of workpiece surface profile | |
| CN107679458A (en) | The extracting method of roadmarking in a kind of road color laser point cloud based on K Means | |
| CN114820918A (en) | Scrap steel pile three-dimensional modeling method based on point cloud data | |
| Han et al. | Automated extraction of rail point clouds by multi-scale dimensional features from MLS data | |
| Dos Santos et al. | Building boundary extraction from LiDAR data using a local estimated parameter for alpha shape algorithm | |
| Jiang et al. | Effective and robust corrugated beam guardrail detection based on mobile laser scanning data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20110504 Termination date: 20211024 |