activity
20182022
most citedLearning for Multi-Model and Multi-Type Fitting

4 citations · 6 across the 5 of their papers we have counts for

collaborators

14 papers

cs.CV20222 cited

Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images

Chao Wen, Yinda Zhang, Chenjie Cao +3

We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses. While many previous works learn to hallucinate…

cs.CV2019

Video Depth Estimation by Fusing Flow-to-Depth Proposals

Jiaxin Xie, Chenyang Lei, Zhuwen Li +2

Depth from a monocular video can enable billions of devices and robots with a single camera to see the world in 3D. In this paper, we present an approach with a differentiable flow…

cs.CV2019

DeepSFM: Structure From Motion Via Deep Bundle Adjustment

Xingkui Wei, Yinda Zhang, Zhuwen Li +2

Structure from motion (SfM) is an essential computer vision problem which has not been well handled by deep learning. One of the promising trends is to apply explicit structural co…

cs.CV2019

Neural Point Cloud Rendering via Multi-Plane Projection

Peng Dai, Yinda Zhang, Zhuwen Li +2

We present a new deep point cloud rendering pipeline through multi-plane projections. The input to the network is the raw point cloud of a scene and the output are image or image s…

cs.CV2019

PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds

Wenxuan Wu, Zhiyuan Wang, Zhuwen Li +2

We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped…

cs.CV2019

Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness

Shuo Cheng, Zexiang Xu, Shilin Zhu +4

We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D reconstruction from multiple RGB images. Multi-view stereo (MVS) aims to reconstruct fine-grained scene geomet…