# Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity Source: [Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:07:48.255Z Authors: Qi Chen, Sourabh Vora Published: 2022-04-19T08:39:37.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()