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20182022
most citedRethinking Sampling in 3D Point Cloud Generative Adversarial Networks

14 citations · 58 across the 13 of their papers we have counts for

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21 papers · 1 filter

cs.CV20223 cited

Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild

Jiayi Chen, Mi Yan, Jiazhao Zhang +6

In this work, we tackle the challenging task of jointly tracking hand object pose and reconstructing their shapes from depth point cloud sequences in the wild, given the initial po…

cs.CV20222 cited

Multi-Robot Active Mapping via Neural Bipartite Graph Matching

Kai Ye, Siyan Dong, Qingnan Fan +5

We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimati…

cs.CV2022

CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

Tianchen Zhao, Niansong Zhang, Xuefei Ning +3

Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on…

cs.CV202213 cited

PartAfford: Part-level Affordance Discovery from 3D Objects

Chao Xu, Yixin Chen, He Wang +3

Understanding what objects could furnish for humans-namely, learning object affordance-is the crux to bridge perception and action. In the vision community, prior work primarily fo…

cs.CV202111 cited

Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation

Xiaolong Li, Yijia Weng, Li Yi +4

Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models. To reduce the hu…

cs.CV2021

ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

Yanchao Yang, Hanxiang Ren, He Wang +5

We describe an unsupervised domain adaptation method for image content shift caused by viewpoint changes for a semantic segmentation task. Most existing methods perform domain alig…