3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D point clouds that were obtained using a LiDAR scan...

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Veröffentlicht in:	2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition S. 9224 - 9232
Hauptverfasser:	Graham, Benjamin, Engelcke, Martin, Maaten, Laurens van der
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2018
Schlagworte:	Convolution Convolutional codes Image segmentation Memory management Semantics Stationary state Three-dimensional displays
ISSN:	1063-6919
Online-Zugang:	Volltext
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