This repository contains MATLAB codes and data for sparse depth sensing, the problem of dense depth image reconstruction from very limited amount of measurements. Please refer to our paper Sparse Depth Sensing for Resource-Constrained Robots for more details.
- The code is self-contained. No installation is required.
- However, if you are interested in trying out a differnet solver CVX, please download it from this link and follow the installation instructions there. CVX is a Matlab-based modeling system for convex optimization and it has slight higher accuracy than our fast solver NESTA. We recommend obtaining an academic license for the most optimized performance.
- run
demo_single_frame.mfor a simple demo of the reconstruction algorithm on each single frame of depth images. - run
demo_multi_frame.mfor a simple demo of the reconstruction algorithm on samples collected across multiple frames.
The code is structured as follows.
lib/algorithmcontains the core code, i.e., the formulation of our optimization problem (inl1ReconstructionOnImage.m).lib/nesta_solveris the implementation of the NESTA fast solver tailored to our problem.lib/geometryhandles all geometry related implementations (e.g., rigid body transformation, image projection).lib/samplingprovides functions for create a small set of measurements from the ground truth depth image.lib/utilitycontains other helper functions.
The data folder contains two datasets, including
- ZED: rgb images and depth images collected from the ZED Stereo Camera.
- lids_floor6: rgb and depth images collected from the Kinect sensor, along with odometry information obtained from a odometer.
If you use our code in your research, please consider citing:
@inproceedings{ma2016sparse,
title={Sparse sensing for resource-constrained depth reconstruction},
author={Ma, Fangchang and Carlone, Luca and Ayaz, Ulas and Karaman, Sertac},
booktitle={Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on},
pages={96--103},
year={2016},
organization={IEEE}
}
Please email Fangchang Ma (fcma@mit.edu) for problems and bugs. Thanks!