Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks

22 July 2020

Papers citing "Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks"

7 / 7 papers shown

Title
Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation Elena Camuffo Umberto Michieli Simone Milani 3DPC 22 4 0 26 Jan 2023
Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition Axel Berg Magnus Oskarsson Mark O'Connor 3DPC ViT 11 26 0 08 Apr 2022
3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification Dening Lu Qian Xie Linlin Xu Jonathan Li 3DV 8 65 0 02 Mar 2022
PU-Transformer: Point Cloud Upsampling Transformer Shi Qiu Saeed Anwar Nick Barnes 3DPC ViT 8 50 0 24 Nov 2021
PointMixer: MLP-Mixer for Point Cloud Understanding Jaesung Choe Chunghyun Park François Rameau Jaesik Park In So Kweon 3DPC 26 98 0 22 Nov 2021
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters Yifan Xu Tianqi Fan Mingye Xu Long Zeng Yu Qiao 3DV 3DPC 145 760 0 30 Mar 2018
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 261 10,106 0 16 Nov 2016