no code implementations • 12 Aug 2020 • Di Qiu, Jin Zeng, Zhanghan Ke, Wenxiu Sun, Chengxi Yang
By incorporating the depth map, our approach is able to extrapolate realistic high-frequency effects under novel lighting via geometry guided image decomposition from the flashlight image, and predict the cast shadow map from the shadow-encoding transformed depth map.
no code implementations • CVPR 2020 • Rui Liu, Chengxi Yang, Wenxiu Sun, Xiaogang Wang, Hongsheng Li
Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias.
1 code implementation • ICCV 2019 • Di Qiu, Jiahao Pang, Wenxiu Sun, Chengxi Yang
Recently, it is increasingly popular to equip mobile RGB cameras with Time-of-Flight (ToF) sensors for active depth sensing.
no code implementations • 25 Sep 2018 • Ruichao Xiao, Wenxiu Sun, Chengxi Yang
Intuitively, the vari-ance in the Laplacian distribution is large for low confidentpixels while small for high-confidence pixels.
1 code implementation • CVPR 2018 • Jiahao Pang, Wenxiu Sun, Chengxi Yang, Jimmy Ren, Ruichao Xiao, Jin Zeng, Liang Lin
By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details.
1 code implementation • 30 Aug 2017 • Jiahao Pang, Wenxiu Sun, Jimmy SJ. Ren, Chengxi Yang, Qiong Yan
As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement.