Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity

• URL: http://arxiv.org/abs/2204.08744v1
• Date: Tue, 19 Apr 2022 08:39:37 GMT
• Title: Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity
• Authors: Qi Chen and Sourabh Vora
• Abstract summary: We jointly optimize semantic segmentation and class-agnostic instance classification in a single network. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods.
• Score: 10.378476897786571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pillar-based bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods and is comparable to proposal-based methods which requires extra annotation from object detection.

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