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Multimodal Panoptic Segmentation of 3D Point Clouds

By:

Dï¿½rr, Fabian [auth]

Material type: Text

TextLanguage: English Series: Publication details: KIT Scientific Publishing 2023Description: 1 electronic resource (248 p.)Content type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

KSP/1000161158

Subject(s):

Maths for computer scientists
Temporal Fusion; Sensor Fusion; Semantic Segmentation; Panoptic Segmentation; Zeitliche Fusion; Semantische Segmentierung; Panoptische Segmentierung; Sensorfusion; Deep Learning

Online resources:

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.

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Item type	Home library	Collection	Call number	Materials specified	Status	Date due	Barcode
Electronic-Books	OPJGU Sonepat- Campus	E-Books Open Access			Available

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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 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.