3D-ANC: Adaptive Neural Collapse for Robust 3D Point Cloud Recognition

Fuente: arXiv

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Bibliographic Details
Main Authors:	Huang, Yuanmin, Li, Wenxuan, Zhang, Mi, Zhang, Xiaohan, You, Xiaoyu, Yang, Min
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Cryptography and Security
Online Access:	Acceder al recurso
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author	Huang, Yuanmin Li, Wenxuan Zhang, Mi Zhang, Xiaohan You, Xiaoyu Yang, Min
author_facet	Huang, Yuanmin Li, Wenxuan Zhang, Mi Zhang, Xiaohan You, Xiaoyu Yang, Min
contents	Deep neural networks have recently achieved notable progress in 3D point cloud recognition, yet their vulnerability to adversarial perturbations poses critical security challenges in practical deployments. Conventional defense mechanisms struggle to address the evolving landscape of multifaceted attack patterns. Through systematic analysis of existing defenses, we identify that their unsatisfactory performance primarily originates from an entangled feature space, where adversarial attacks can be performed easily. To this end, we present 3D-ANC, a novel approach that capitalizes on the Neural Collapse (NC) mechanism to orchestrate discriminative feature learning. In particular, NC depicts where last-layer features and classifier weights jointly evolve into a simplex equiangular tight frame (ETF) arrangement, establishing maximally separable class prototypes. However, leveraging this advantage in 3D recognition confronts two substantial challenges: (1) prevalent class imbalance in point cloud datasets, and (2) complex geometric similarities between object categories. To tackle these obstacles, our solution combines an ETF-aligned classification module with an adaptive training framework consisting of representation-balanced learning (RBL) and dynamic feature direction loss (FDL). 3D-ANC seamlessly empowers existing models to develop disentangled feature spaces despite the complexity in 3D data distribution. Comprehensive evaluations state that 3D-ANC significantly improves the robustness of models with various structures on two datasets. For instance, DGCNN's classification accuracy is elevated from 27.2% to 80.9% on ModelNet40 -- a 53.7% absolute gain that surpasses leading baselines by 34.0%.
format	Preprint
id	arxiv_https___arxiv_org_abs_2511_07040
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	3D-ANC: Adaptive Neural Collapse for Robust 3D Point Cloud Recognition Huang, Yuanmin Li, Wenxuan Zhang, Mi Zhang, Xiaohan You, Xiaoyu Yang, Min Computer Vision and Pattern Recognition Cryptography and Security Deep neural networks have recently achieved notable progress in 3D point cloud recognition, yet their vulnerability to adversarial perturbations poses critical security challenges in practical deployments. Conventional defense mechanisms struggle to address the evolving landscape of multifaceted attack patterns. Through systematic analysis of existing defenses, we identify that their unsatisfactory performance primarily originates from an entangled feature space, where adversarial attacks can be performed easily. To this end, we present 3D-ANC, a novel approach that capitalizes on the Neural Collapse (NC) mechanism to orchestrate discriminative feature learning. In particular, NC depicts where last-layer features and classifier weights jointly evolve into a simplex equiangular tight frame (ETF) arrangement, establishing maximally separable class prototypes. However, leveraging this advantage in 3D recognition confronts two substantial challenges: (1) prevalent class imbalance in point cloud datasets, and (2) complex geometric similarities between object categories. To tackle these obstacles, our solution combines an ETF-aligned classification module with an adaptive training framework consisting of representation-balanced learning (RBL) and dynamic feature direction loss (FDL). 3D-ANC seamlessly empowers existing models to develop disentangled feature spaces despite the complexity in 3D data distribution. Comprehensive evaluations state that 3D-ANC significantly improves the robustness of models with various structures on two datasets. For instance, DGCNN's classification accuracy is elevated from 27.2% to 80.9% on ModelNet40 -- a 53.7% absolute gain that surpasses leading baselines by 34.0%.
title	3D-ANC: Adaptive Neural Collapse for Robust 3D Point Cloud Recognition
topic	Computer Vision and Pattern Recognition Cryptography and Security
url	https://arxiv.org/abs/2511.07040