Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Yanhao Wu, Tong Zhang, Wei Ke, Congpei Qiu, Sabine Süsstrunk, Mathieu Salzmann; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 23052-23061

In the realm of point cloud scene understanding particularly in indoor scenes objects are arranged following human habits resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can create a tendency for neural networks to exploit these strong dependencies bypassing the individual object patterns. To address this challenge we introduce a novel self-supervised learning (SSL) strategy. Our approach leverages both object patterns and contextual cues to produce robust features. It begins with the formulation of an object-exchanging strategy where pairs of objects with comparable sizes are exchanged across different scenes effectively disentangling the strong contextual dependencies. Subsequently we introduce a context-aware feature learning strategy which encodes object patterns without relying on their specific context by aggregating object features across various scenes. Our extensive experiments demonstrate the superiority of our method over existing SSL techniques further showing its better robustness to environmental changes. Moreover we showcase the applicability of our approach by transferring pre-trained models to diverse point cloud datasets.