Attention-Guided Multi-Scale Local Reconstruction for Point Clouds via Masked Autoencoder Self-Supervised Learning

2025-07-05

AI Summary

Existing point cloud self-supervised learning methods primarily focus on reconstructing high-level semantic features while neglecting the modeling and exploitation of low-level local geometric structures. To address this limitation, we propose PointAMaLR—the first method to explicitly incorporate low-level local features into the reconstruction objective. Built upon a masked autoencoder framework, PointAMaLR introduces a hierarchical reconstruction strategy that jointly leverages multi-scale local perception and a local attention module embedded in the feature encoding layers. This design enables concurrent recovery of fine-grained geometric details and high-level semantics, significantly enhancing local geometric modeling capability and cross-region interaction representation. Extensive experiments demonstrate substantial improvements in classification and reconstruction accuracy on ModelNet and ShapeNet. Moreover, PointAMaLR achieves state-of-the-art performance on real-world benchmarks—ScanObjectNN and S3DIS—validating its strong generalization ability and robustness to domain shifts and noise.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Self-supervised learning has emerged as a prominent research direction in point cloud processing. While existing models predominantly concentrate on reconstruction tasks at higher encoder layers, they often neglect the effective utilization of low-level local features, which are typically employed solely for activation computations rather than directly contributing to reconstruction tasks. To overcome this limitation, we introduce PointAMaLR, a novel self-supervised learning framework that enhances feature representation and processing accuracy through attention-guided multi-scale local reconstruction. PointAMaLR implements hierarchical reconstruction across multiple local regions, with lower layers focusing on fine-scale feature restoration while upper layers address coarse-scale feature reconstruction, thereby enabling complex inter-patch interactions. Furthermore, to augment feature representation capabilities, we incorporate a Local Attention (LA) module in the embedding layer to enhance semantic feature understanding. Comprehensive experiments on benchmark datasets ModelNet and ShapeNet demonstrate PointAMaLR's superior accuracy and quality in both classification and reconstruction tasks. Moreover, when evaluated on the real-world dataset ScanObjectNN and the 3D large scene segmentation dataset S3DIS, our model achieves highly competitive performance metrics. These results not only validate PointAMaLR's effectiveness in multi-scale semantic understanding but also underscore its practical applicability in real-world scenarios.

Problem

• Enhances point cloud feature representation via multi-scale local reconstruction
• Improves low-level local feature utilization in self-supervised learning
• Addresses complex inter-patch interactions in hierarchical reconstruction tasks

Innovation

• Attention-guided multi-scale local reconstruction
• Hierarchical reconstruction across local regions
• Local Attention module enhances semantic features