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Rist et al., 2020 - Google Patents

Scssnet: Learning spatially-conditioned scene segmentation on lidar point clouds

Rist et al., 2020

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Document ID
8665345624487200963
Author
Rist C
Schmidt D
Enzweiler M
Gavrila D
Publication year
Publication venue
2020 IEEE Intelligent Vehicles Symposium (IV)

External Links

Snippet

This work proposes a spatially-conditioned neural network to perform semantic segmentation and geometric scene completion in 3D on real-world LiDAR data. Spatially- conditioned scene segmentation (SCSSnet) is a representation suitable to encode …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
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    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
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    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06T15/00—3D [Three Dimensional] image rendering
    • G06T15/04—Texture mapping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—3D [Three Dimensional] image rendering
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    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
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