Skip to content
Sparse Cost Volume for Efficient Stereo Matching
Python
Branch: master
Clone or download
Latest commit 338a917 Nov 6, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
SCVNet Delete readme Nov 6, 2018
LICENSE Initial commit Nov 6, 2018
README.md Update README.md Nov 6, 2018

README.md

SCV-Net - Sparse Cost Volume for Efficient Stereo Matching

Our work is build on the GC-Net. The GC-Net is proposed in "End-to-end learning of geometry and context for deep stereo regression", by Kendall et al. in ICCV 2017. By making the cost volume compact and proposing an efficient similarity evaluation, we achieved faster stereo matching while improving the accuracy. Moreover, we proposed to use weight normalization instead of batch normalization. This improved the performance at dim and noise regions. Finally, we achieved 70% GPU memory and 60% processing time reducing, while improving the matching accuracy (3PE on the KITTI 2015 Dataset, GC-Net: 2.87% -> Ours: 2.61%).

System requirement

----Python 3.6

--------PyTorch 0.3.0

--------torchvision 0.1.8

--------pypng 0.0.18

--------pillow 4.2.1

--------numpy 1.13.3

--------matplotlab 2.1.0

Note

In "SCVNet/SCVNet.py", replace

----SCENE_FLOW_TRAIN_PATH_IMAGE

----SCENE_FLOW_TRAIN_PATH_LABEL

----SCENE_FLOW_TEST_PATH_IMAGE

----SCENE_FLOW_TEST_PATH_LABEL

----KITTI_2015_TRAIN_PATH_IMAGE

----KITTI_2015_TRAIN_PATH_LABEL

----KITTI_2015_TEST_PATH_IMAGE

----KITTI_2015_TEST_PATH_LABEL

with your own path.

You can’t perform that action at this time.