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

arXiv:2404.07504v1 Announce Type: new
Abstract: 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. …

abstract arxiv cloud correlations cs.ai cs.cv dependencies exploit habits human improving networks neural networks object objects realm self-supervised learning semantics supervised learning through type understanding