HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis

• URL: http://arxiv.org/abs/2504.14132v1
• Date: Sat, 19 Apr 2025 01:33:19 GMT
• Title: HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis
• Authors: Xuanhua Yin, Dingxin Zhang, Jianhui Yu, Weidong Cai,
• Abstract summary: We introduce Handcrafted Feature-Based Rotation-Invariant Masked Autoencoder (HFBRI-MAE)<n>HFBRI-MAE is a novel framework that refines the MAE design with rotation-invariant handcrafted features to ensure stable feature learning across different orientations.<n>We show that HFBRI-MAE consistently outperforms existing methods in object classification, segmentation, and few-shot learning.
• Score: 10.978894026853675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) has demonstrated remarkable success in 3D point cloud analysis, particularly through masked autoencoders (MAEs). However, existing MAE-based methods lack rotation invariance, leading to significant performance degradation when processing arbitrarily rotated point clouds in real-world scenarios. To address this limitation, we introduce Handcrafted Feature-Based Rotation-Invariant Masked Autoencoder (HFBRI-MAE), a novel framework that refines the MAE design with rotation-invariant handcrafted features to ensure stable feature learning across different orientations. By leveraging both rotation-invariant local and global features for token embedding and position embedding, HFBRI-MAE effectively eliminates rotational dependencies while preserving rich geometric structures. Additionally, we redefine the reconstruction target to a canonically aligned version of the input, mitigating rotational ambiguities. Extensive experiments on ModelNet40, ScanObjectNN, and ShapeNetPart demonstrate that HFBRI-MAE consistently outperforms existing methods in object classification, segmentation, and few-shot learning, highlighting its robustness and strong generalization ability in real-world 3D applications.

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