activity
20182020
most citedStereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV20201 cited

Towards Geometry Guided Neural Relighting with Flash Photography

Di Qiu, Jin Zeng, Zhanghan Ke +2

Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs nontrivial effo…

cs.CV20202 cited

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

Rui Liu, Chengxi Yang, Wenxiu Sun +2

Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. Although unsupervised image-to-image translation networks represented by C…

cs.CV2019

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

Di Qiu, Jiahao Pang, Wenxiu Sun +1

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…

cs.CV2018

Confidence Inference for Focused Learning in Stereo Matching

Ruichao Xiao, Wenxiu Sun, Chengxi Yang

In this paper, we present confidence inference approachin an unsupervised way in stereo matching. Deep Neu-ral Networks (DNNs) have recently been achieving state-of-the-art perform…

cs.CV2018

Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains

Jiahao Pang, Wenxiu Sun, Chengxi Yang +4

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…