Panoster：LiDAR点群のエンドツーエンドパノプティックセグメンテーション
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds
LiDAR点群に対し、学習ベースのクラスタリングでインスタンスを識別する提案不要のパノプティックセグメンテーション手法を提案し、SemanticKITTIで最先端性能を達成した。
著者: Stefano Gasperini, Mohammad-Ali Nikouei Mahani, Alvaro Marcos-Ramiro, Nassir Navab, Federico Tombari
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and efficient perception systems. In this paper, we present Panoster, a novel proposal-free panoptic segmentation method for LiDAR point clouds. Unlike previous approaches relying on several steps to group pixels or points into objects, Panoster proposes a simplified framework incorporating a learning-based clustering solution to identify instances. At inference time, this acts as a class-agnostic segmentation, allowing Panoster to be fast, while outperforming prior methods in terms of accuracy. Without any post-processing, Panoster reached state-of-the-art results among published approaches on the challenging SemanticKITTI benchmark, and further increased its lead by exploiting heuristic techniques. Additionally, we showcase how our method can be flexibly and effectively applied on diverse existing semantic architectures to deliver panoptic predictions.
