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Wenxuan Wu

5 papers hereh-index 82.1k citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.RO1
same name
  • Wenxuan Wu — 9 papers, h 4
  • Wenxuan Wu — 4 papers, h 4
  • Wenxuan Wu — 2 papers, h 5
  • Wenxuan Wu — 1 paper, h 1
  • Wenxuan Wu — 1 paper, h 2
  • Wenxuan Wu — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedPointConv: Deep Convolutional Networks on 3D Point Clouds

102 citations · 169 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation

Shenwang Jiang, Jianan Li, Ying Wang +4

Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation. Previou…

cs.CV2022★ 1 cited

PointConvFormer: Revenge of the Point-based Convolution

Wenxuan Wu, Li Fuxin, Qi Shan

We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point…

cs.CV2019★ 66 cited

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.CV2018★ 102 cited

PointConv: Deep Convolutional Networks on 3D Point Clouds

Wenxuan Wu, Zhongang Qi, Li Fuxin

Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we exte…

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