10 citations · 28 across the 5 of their papers we have counts for
5 papers · 1 filter
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
Xiaotian Chen, Yuwang Wang, Xuejin Chen +1
Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the dept…
Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
Xuanchi Ren, Tao Yang, Yuwang Wang +1
Content and style (C-S) disentanglement intends to decompose the underlying explanatory factors of objects into two independent subspaces. From the unsupervised disentanglement per…
Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments
Junsheng Zhou, Yuwang Wang, Kaihuai Qin +1
Recently unsupervised learning of depth from videos has made remarkable progress and the results are comparable to fully supervised methods in outdoor scenes like KITTI. However, t…
Unsupervised High-Resolution Depth Learning From Videos With Dual Networks
Junsheng Zhou, Yuwang Wang, Kaihuai Qin +1
Unsupervised depth learning takes the appearance difference between a target view and a view synthesized from its adjacent frame as supervisory signal. Since the supervisory signal…
Adversarial View-Consistent Learning for Monocular Depth Estimation
Yixuan Liu, Yuwang Wang, Shengjin Wang
This paper addresses the problem of Monocular Depth Estimation (MDE). Existing approaches on MDE usually model it as a pixel-level regression problem, ignoring the underlying geome…