Multimodal Panoptic Segmentation of 3D Point Clouds

Multimodal Panoptic Segmentation of 3D Point Clouds

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The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal ap...

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Bibliographic Details
Main Author: Dürr, Fabian (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2023
Series:Karlsruher Schriften zur Anthropomatik
Subjects:
Maths for computer scientists
Temporal Fusion; Sensor Fusion; Semantic Segmentation; Panoptic Segmentation; Zeitliche Fusion; Semantische Segmentierung; Panoptische Segmentierung; Sensorfusion; Deep Learning
Online Access:DOAB: download the publication
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Summary:The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.
Physical Description:1 electronic resource (248 p.)
ISBN:KSP/1000161158
Access:Open Access

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