提出旋转不变的点云自监督模型，提升真实场景下的泛化能力。
HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis
- 用手工设计的旋转不变特征重构点云嵌入
- 在多个数据集上分类/分割性能超越现有方法
- 适合需要稳定处理任意方向点云的应用场景
自监督学习在3D点云分析中取得显著进展，尤其依赖掩码自编码器（MAE）。然而，现有基于MAE的方法缺乏旋转不变性，在真实场景中处理任意方向点云时性能大幅下降。为此，本文提出手工作特征驱动的旋转不变掩码自编码器（HFBRI-MAE），通过引入旋转不变的局部与全局特征进行标记嵌入和位置嵌入，有效消除旋转依赖性，同时保留丰富的几何结构。此外，将重建目标重新定义为输入的规范对齐版本，缓解旋转歧义。在ModelNet40、ScanObjectNN和ShapeNetPart上的大量实验表明，HFBRI-MAE在物体分类、分割和少样本学习任务中持续优于现有方法，展现出强大的鲁棒性和泛化能力。
原文摘要 · Abstract (English)
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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