GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D

2024-05-20

arXiv.org

AI Summary

This work addresses the limitation of conventional random masking in point cloud self-supervised learning, which disregards intrinsic geometric structure and thus hinders representation learning. To this end, we propose GeoMask3D—a geometry-aware masking strategy. Its core innovations are twofold: (1) the first learnable geometric complexity metric, which guides the selection of mask regions based on structural intricacy; and (2) a feature-level full-partial knowledge distillation mechanism within a teacher-student framework, enabling context-guided geometric complexity prediction. GeoMask3D is seamlessly integrated into the masked autoencoder (MAE) paradigm, substantially enhancing the model’s capacity to capture fine-grained local geometry. Extensive experiments on benchmarks including ModelNet40 demonstrate state-of-the-art performance in both classification and few-shot recognition tasks, validating that geometry-aware masking delivers substantial and consistent gains for downstream performance.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We introduce a pioneering approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE). Unlike the conventional method of random masking, our technique utilizes a teacher-student model to focus on intricate areas within the data, guiding the model's focus toward regions with higher geometric complexity. This strategy is grounded in the hypothesis that concentrating on harder patches yields a more robust feature representation, as evidenced by the improved performance on downstream tasks. Our method also presents a complete-to-partial feature-level knowledge distillation technique designed to guide the prediction of geometric complexity utilizing a comprehensive context from feature-level information. Extensive experiments confirm our method's superiority over State-Of-The-Art (SOTA) baselines, demonstrating marked improvements in classification, and few-shot tasks.

Problem

• Enhances self-supervised learning for 3D point clouds
• Focuses on geometrically complex areas for robust features
• Improves performance in classification and few-shot tasks

Innovation

• GeoMask3D enhances self-supervised point cloud learning.
• Teacher-student model focuses on complex geometric regions.
• Complete-to-partial feature-level knowledge distillation improves performance.