Endowing Deep 3D Models with Rotation Invariance Based on Principal
Component Analysis

Endowing Deep 3D Models with Rotation Invariance Based on Principal Component Analysis

20 October 2019

Papers citing "Endowing Deep 3D Models with Rotation Invariance Based on Principal Component Analysis"

10 / 10 papers shown

Title
MaskLRF: Self-supervised Pretraining via Masked Autoencoding of Local Reference Frames for Rotation-invariant 3D Point Set Analysis Takahiko Furuya 3DPC 43 2 0 01 Mar 2024
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds O. Melia Eric Jonas Rebecca Willett OOD 3DPC 20 3 0 27 Jul 2023
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance Yiming Cui Lecheng Ruan Hang Dong Qiang Li Zhongming Wu T. Zeng Fengyu Fan 3DPC 35 0 0 13 May 2023
General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment Liang Xie Yibo Yang Wenxiao Wang Binbin Lin Deng Cai Xiaofei He Ronghua Liang 3DPC 24 2 0 20 Feb 2023
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis Pavlo Melnyk Andreas Robinson M. Felsberg Maarten Wadenback 3DPC 23 2 0 26 Nov 2022
Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds Zhiyuan Zhang Yuchao Dai Bin Fan Jiadai Sun Mingyi He 3DPC 46 7 0 26 Oct 2022
A Simple Strategy to Provable Invariance via Orbit Mapping Kanchana Vaishnavi Gandikota Jonas Geiping Zorah Lähner Adam Czapliñski Michael Moeller AAML 3DPC 18 3 0 24 Sep 2022
Frame Averaging for Equivariant Shape Space Learning Matan Atzmon Koki Nagano Sanja Fidler S. Khamis Y. Lipman FedML 36 13 0 03 Dec 2021
Triangle-Net: Towards Robustness in Point Cloud Learning Chenxi Xiao J. Wachs 3DH 3DPC 31 34 0 27 Feb 2020
A Decomposable Attention Model for Natural Language Inference Ankur P. Parikh Oscar Täckström Dipanjan Das Jakob Uszkoreit 213 1,367 0 06 Jun 2016