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Towards accurate instance segmentation in large-scale LiDAR point clouds

2023-07-06 · Binbin Xiang, Torben Peters, Theodora Kontogianni, Frawa Vetterli, Stefano Puliti, Rasmus Astrup, Konrad Schindler

Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy.

📄 PDF Abstract BibTeX arXiv:2307.02877

Code (1)

bxiang233/panopticsegforlargescalepointcloud 공식 구현 pytorch

Tasks

ClusteringInstance Segmentation ManagementObjectPanoptic Segmentation Scene Understanding Segmentation Semantic Segmentation

CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds

2021-11-02 · Enxu Li, Ryan Razani, YiXuan Xu, Bingbing Liu

A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes. Existing approaches usually rely on proposals or cluste…

Autonomous Driving Clustering Decoder Depth Completion+4

EfficientLPS: Efficient LiDAR Panoptic Segmentation

2021-02-16 · Kshitij Sirohi, Rohit Mohan, Daniel Büscher, Wolfram Burgard 외

Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existing top-down approaches tackle this probl…

Autonomous Vehicles Instance Segmentation Panoptic Segmentation Segmentation+1

ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data

2023-03-08 · Wenbang Deng, Kaihong Huang, Qinghua Yu, Huimin Lu 외

Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these instances have been labeled in the trai…

Autonomous Navigation Clustering Instance Segmentation Open-World Instance Segmentation+3

A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI

2020-03-04 · Jens Behley, Andres Milioto, Cyrill Stachniss

Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providin…

Instance Segmentation Panoptic Segmentation Segmentation Semantic Segmentation

Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking

2021-09-08 · Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou 외

Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments. As LiDARs provide accurate illumination-independent geometric depictions of t…

Benchmarking Diversity Navigate Panoptic Segmentation+3