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Title:

Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds

Author(s):
Gasperini, S.; Mahani, M.-A. N.; Marcos-Ramiro, A.; Navab, N.; Tombari, F.
Abstract:
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 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...     »
Keywords:
ICRA,Deep Learning,Clustering,Autonomous Driving,Panoptic Segmentation,LiDAR,point cloud
Year:
2020
Language:
de
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