[go: up one dir, main page]

Pan et al., 2022 - Google Patents

Activenerf: Learning where to see with uncertainty estimation

Pan et al., 2022

View PDF
Document ID
7696752785009743824
Author
Pan X
Lai Z
Song S
Huang G
Publication year
Publication venue
European Conference on Computer Vision

External Links

Snippet

Abstract Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective, the performance of NeRF is highly influenced by the quality of training …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image

Similar Documents

Publication Publication Date Title
Pan et al. Activenerf: Learning where to see with uncertainty estimation
Lin et al. Deep global-relative networks for end-to-end 6-dof visual localization and odometry
Tosi et al. A survey on deep stereo matching in the twenties
WO2022201212A1 (en) Method for determining the depth from a single image and system thereof
Li et al. A review of advances in image inpainting research
Zhao et al. TransDehaze: transformer-enhanced texture attention for end-to-end single image dehaze
Yu et al. UTDM: a universal transformer-based diffusion model for multi-weather-degraded images restoration
Sun et al. Deblurring transformer tracking with conditional cross-attention
Agrahari Baniya et al. Frame selection using spatiotemporal dynamics and key features as input pre-processing for video super-resolution models
Liu et al. Fast simultaneous image super-resolution and motion deblurring with decoupled cooperative learning
Barra et al. Gradient boosting regression for faster Partitioned Iterated Function Systems‐based head pose estimation
Yan et al. SAS: A general framework induced by sequence association for shape from focus
Liu et al. SimpliFusion: a simplified infrared and visible image fusion network
Wu et al. Few-shot stereo matching with high domain adaptability based on adaptive recursive network
Zhou et al. Underwater occluded object recognition with two-stage image reconstruction strategy
He et al. Toward accurate and robust pedestrian detection via variational inference
Yang et al. Unsupervised image blind super resolution via real degradation feature learning
Xi et al. Learning filter selection policies for interpretable image denoising in parametrised action space
Paramanandam et al. A review on deep learning techniques for saliency detection
Zhang et al. Continuous learning deraining network based on residual FFT convolution and contextual transformer module
Kumar et al. Visual tracking using structural local DCT sparse appearance model with occlusion detection
Chen et al. Recursive noisy label learning paradigm based on confidence measurement for semi-supervised depth completion
Li et al. Feature disentanglement and adaptive fusion for improving multi-modal tracking
Yu et al. Multi-view stereo by fusing monocular and a combination of depth representation methods
Pividori et al. Exploiting gan internal capacity for high-quality reconstruction of natural images