DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch (ICCV 2019)
This repository releases code for our paper DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch.
DeepPruner
Differentiable Patch Match
Requirements (Major Dependencies)
Citation
An efficient "Real Time Stereo Matching" algorithm, which takes as input 2 images and outputs a disparity (or depth) map.
Results/ Metrics:
ETH3D: SOTA among all ROB entries.
SceneFlow: 2nd among all published algorithms, while being 8x faster than the 1st.
Runtime: 62ms (for DeepPruner-fast), 180ms (for DeepPruner-best)
Cuda Memory Requirements: 805MB (for DeepPruner-best)
More details in the corresponding folder README.
If you use our source code, or our paper, please consider citing the following:
@inproceedings{Duggal2019ICCV,
title = {DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch},
author = {Shivam Duggal and Shenlong Wang and Wei-Chiu Ma and Rui Hu and Raquel Urtasun},
booktitle = {ICCV},
year = {2019} }
Correspondences to Shivam Duggal [email protected], Shenlong Wang [email protected], Wei-Chiu Ma [email protected]