Point Cloud Mixture-of-Domain-Experts Model for 3D Self-supervised Learning

• URL: http://arxiv.org/abs/2410.09886v3
• Date: Tue, 03 Jun 2025 01:21:46 GMT
• Title: Point Cloud Mixture-of-Domain-Experts Model for 3D Self-supervised Learning
• Authors: Yaohua Zha, Tao Dai, Hang Guo, Yanzi Wang, Bin Chen, Ke Chen, Shu-Tao Xia,
• Abstract summary: Point clouds, as a primary representation of 3D data, can be categorized into scene domain point clouds and object domain point clouds.<n>In this paper, we propose to learn a comprehensive Point cloud Mixture-of-Domain-Experts model (Point-MoDE) via a block-to-scene pre-training strategy.
• Score: 50.55005524072687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds, as a primary representation of 3D data, can be categorized into scene domain point clouds and object domain point clouds. Point cloud self-supervised learning (SSL) has become a mainstream paradigm for learning 3D representations. However, existing point cloud SSL primarily focuses on learning domain-specific 3D representations within a single domain, neglecting the complementary nature of cross-domain knowledge, which limits the learning of 3D representations. In this paper, we propose to learn a comprehensive Point cloud Mixture-of-Domain-Experts model (Point-MoDE) via a block-to-scene pre-training strategy. Specifically, we first propose a mixture-of-domain-expert model consisting of scene domain experts and multiple shared object domain experts. Furthermore, we propose a block-to-scene pretraining strategy, which leverages the features of point blocks in the object domain to regress their initial positions in the scene domain through object-level block mask reconstruction and scene-level block position regression. By integrating the complementary knowledge between object and scene, this strategy simultaneously facilitates the learning of both object-domain and scene-domain representations, leading to a more comprehensive 3D representation. Extensive experiments in downstream tasks demonstrate the superiority of our model.

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