Panoptic segmentation of 3D point clouds with Gaussian mixture model in outdoor scenes

Abstract

Accurate panoptic segmentation of 3D point clouds in outdoor scenes is critical for the success of applications such as autonomous driving and robot navigation. Existing methods in this area typically assume that the differences between instances are greater than the differences between points belonging to the same instance and use heuristic techniques for segmentation. However, this assumption may not hold in real scenes with occlusion and noise. In addition, most of the previous methods formulate point-wise embedding learning and instance clustering as two decoupled steps for separate optimization, making it a challenging task to learn discriminative embeddings. To address these issues, we introduce a framework for modeling points belonging to the same instance using learnable Gaussian distributions and formulate the point cloud as a Gaussian mixture model. Based on this formulation, we introduce a unified loss function that links the embedding learning and instance clustering in an end-to-end manner. Our framework is generic and can be seamlessly incorporated with existing panoptic segmentation networks. By explicitly modeling intra-instance variance and leveraging end-to-end optimization, our framework improves the discrimination capability of point embeddings with higher accuracy and robustness. Extensive experiments on two large-scale benchmarks demonstrate the effectiveness of the proposed method.