← Search

Chengxi Yang

3 accepted papers

2020

StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching

CVPR 2020poster

Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. Although unsupervised image-to-image translation networks represented by CycleGAN show great potential in dealing with domain gap, it is non-trivial to generalize this method to stereo matching…

Cited by 56PDFScholar
2019

Deep End-to-End Alignment and Refinement for Time-of-Flight RGB-D Module

ICCV 2019poster

Recently, it is increasingly popular to equip mobile RGB cameras with Time-of-Flight (ToF) sensors for active depth sensing. However, for off-the-shelf ToF sensors, one must tackle two problems in order to obtain high-quality depth with respect to the RGB camera, namely 1) online calibration and ali…

Cited by 29PDFcodeScholar
2018

Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains

CVPR 2018poster

Despite the recent success of stereo matching with convolutional neural networks (CNNs), it remains arduous to generalize a pre-trained deep stereo model to a novel domain. A major difficulty is to collect accurate ground-truth disparities for stereo pairs in the target domain. In this work, we prop…