Self-supervised pre-training for hybrid point-cloud architectures

Develop self-supervised pre-training methods specifically tailored to hybrid architectures that combine attention mechanisms with state-space models, and determine the compatibility of existing Transformer self-supervised pre-training methods with such hybrid architectures.

Background

PointLearner combines point-focused attention with a context-scan state-space model, creating a hybrid architecture that differs from the Transformer architectures targeted by many existing self-supervised pre-training methods. The paper notes that large-scale self-supervised pre-training has improved Transformer-based point-cloud models, particularly by strengthening local or structural modeling through increased data scale.

The unresolved issue is whether existing Transformer-oriented pre-training methods transfer effectively to attention–state-space hybrid models and what pre-training strategies should be designed specifically for those architectures. The authors identify collecting more data and developing such hybrid-specific self-supervised methods as a direction for future research.