Paper ID: 2409.04760

Training-Free Point Cloud Recognition Based on Geometric and Semantic Information Fusion

Yan Chen, Di Huang, Zhichao Liao, Xi Cheng, Xinghui Li, Lone Zeng

The trend of employing training-free methods for point cloud recognition is becoming increasingly popular due to its significant reduction in computational resources and time costs. However, existing approaches are limited as they typically extract either geometric or semantic features. To address this limitation, we propose a novel method that integrates both geometric and semantic features, thereby enhancing the comprehensive understanding of point clouds. For the geometric branch, we adopt a non-parametric strategy to extract geometric features. In the semantic branch, we leverage a model pre-trained through contrastive learning and aligned with text features to obtain semantic features. Experimental results demonstrate that our method outperforms existing state-of-the-art training-free approaches on several popular benchmark datasets, including ModelNet and ScanObiectNN.

Submitted: Sep 7, 2024