PCPNet Learning Local Shape Properties from Raw Point Clouds

In this paper, we propose PCPNET, a deep‐learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid‐level attributes, e.g., for shape classification or semantic labeling, we suggest a patch‐based...

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Vydáno v:Computer graphics forum Ročník 37; číslo 2; s. 75 - 85
Hlavní autoři: Guerrero, Paul, Kleiman, Yanir, Ovsjanikov, Maks, Mitra, Niloy J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.05.2018
Wiley
Témata:
CCS Concepts
Coding
Computer Science
Computer systems organization → Neural networks
Computer Vision and Pattern Recognition
Computing methodologies → Point‐based models
Curvature
Estimation
Learning
Properties (attributes)
Shape analysis
Three dimensional models
CCS Concepts •Computing methodologies → Point-based models
ISSN:0167-7055, 1467-8659
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Shrnutí:In this paper, we propose PCPNET, a deep‐learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid‐level attributes, e.g., for shape classification or semantic labeling, we suggest a patch‐based learning method, in which a series of local patches at multiple scales around each point is encoded in a structured manner. Our approach is especially well‐adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the presence of strong noise and multi‐scale features. Our main contributions include both a novel multi‐scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well‐structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state‐of‐the‐art techniques. Finally, we demonstrate the utility of our approach in the context of shape reconstruction, by showing how it can be used to extract normal orientation information from point clouds.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13343