EAGLE: Contextual Point Cloud Generation via Adaptive Continuous Normalizing Flow with Self-Attention

Abstract: As 3D point clouds become the prevailing shape representation in computer vision, how to generate high-resolution point clouds has become a pressing issue. Flow-based generative models can effectively perform point cloud generation tasks. However, traditional CNN-based flow architectures rely only on local information to extract features, making it difficult to capture global contextual information. Inspired by the wide adoption of Transformers, we explored the complementary roles of self-attention mechanisms in Transformers, CNN, and continuous normalizing flows. To this end, we propose a probabilistic model via adaptive normalizing flows and self-attention. Our idea leverages self-attention mechanisms to capture global contextual information. We also propose adaptive continuous normalizing flows by introducing adaptive bias correction mechanism. Combined with normalization, the mechanism dynamically handles different input contexts and mitigates potential bias-shift issues from standard initialization. Experimental results demonstrate that EAGLE achieves competitive performance in point cloud generation.