activity
20182022
most citedNeural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint Rendering for the Closed Scene Composed of Pre-Captured Objects

17 citations · 38 across the 10 of their papers we have counts for

collaborators

20 papers

cs.CV20224 cited

DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image

Yijin Li, Xinyang Liu, Wenqi Dong +5

Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy and have been massively deployed on mobile devices for the purposes like autofocus, obstacle detection,…

cs.CV202217 cited

Neural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint Rendering for the Closed Scene Composed of Pre-Captured Objects

Bangbang Yang, Yinda Zhang, Yijin Li +4

We, as human beings, can understand and picture a familiar scene from arbitrary viewpoints given a single image, whereas this is still a grand challenge for computers. We hereby pr…

cs.CV20215 cited

Non-local Recurrent Regularization Networks for Multi-view Stereo

Qingshan Xu, Martin R. Oswald, Wenbing Tao +2

In deep multi-view stereo networks, cost regularization is crucial to achieve accurate depth estimation. Since 3D cost volume filtering is usually memory-consuming, recurrent 2D co…

cs.CV20216 cited

Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering

Bangbang Yang, Yinda Zhang, Yinghao Xu +5

Implicit neural rendering techniques have shown promising results for novel view synthesis. However, existing methods usually encode the entire scene as a whole, which is generally…

cs.CV20211 cited

DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization

Cheng Zhang, Zhaopeng Cui, Cai Chen +4

Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploit…

cs.CV2021

Deep Hybrid Self-Prior for Full 3D Mesh Generation

Xingkui Wei, Zhengqing Chen, Yanwei Fu +2

We present a deep learning pipeline that leverages network self-prior to recover a full 3D model consisting of both a triangular mesh and a texture map from the colored 3D point cl…