  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.
