activity
20172023
most citedDeep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

73 citations · 232 across the 24 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

cs.CV2021

A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks

Yefan Zhou, Yiru Shen, Yujun Yan +2

Neural networks (NN) for single-view 3D reconstruction (SVR) have gained in popularity. Recent work points out that for SVR, most cutting-edge NNs have limited performance on recon…

cs.CV2021★ 17 cited

Learning Distilled Collaboration Graph for Multi-Agent Perception

Yiming Li, Shunli Ren, Pengxiang Wu +3

To promote better performance-bandwidth trade-off for multi-agent perception, we propose a novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and adap…

cs.RO2021★ 1 cited

Projector-Guided Non-Holonomic Mobile 3D Printing

Xuchu Xu, Ziteng Wang, Chen Feng

Fused deposition modeling (FDM) using mobile robots instead of the gantry-based 3D printer enables additive manufacturing at a larger scale with higher speed. This introduces chall…

cs.RO2021

Deep Weakly Supervised Positioning

Ruoyu Wang, Xuchu Xu, Li Ding +2

PoseNet can map a photo to the position where it is taken, which is appealing in robotics. However, training PoseNet requires full supervision, where ground truth positions are non…

cs.CV2021

Self-supervised Spatial Reasoning on Multi-View Line Drawings

Siyuan Xiang, Anbang Yang, Yanfei Xue +2

Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset. Based on the fac…

cs.RO2021

Simultaneous Navigation and Construction Benchmarking Environments

Wenyu Han, Chen Feng, Haoran Wu +5

We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major r…

cs.CV2021★ 4 cited

Fooling LiDAR Perception via Adversarial Trajectory Perturbation

Yiming Li, Congcong Wen, Felix Juefei-Xu +1

LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions. When autonomous vehicl…