Deep Learning for Robust Normal Estimation in Unstructured Point Clouds

Normal estimation in point clouds is a crucial first step for numerous algorithms, from surface reconstruction and scene understanding to rendering. A recurrent issue when estimating normals is to make appropriate decisions close to sharp features, not to smooth edges, or when the sampling density i...

Full description

Saved in:
Bibliographic Details
Published in:Computer graphics forum 2016-08, Vol.35 (5), p.281-290
Main Authors: Boulch, Alexandre, Marlet, Renaud
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Computer graphics
Computer Science
Decisions
Deep learning
Density
Estimating techniques
Image processing systems
Learning
Rendering
Robustness
Sampling
Scaling up
Studies
Three dimensional models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Normal estimation in point clouds is a crucial first step for numerous algorithms, from surface reconstruction and scene understanding to rendering. A recurrent issue when estimating normals is to make appropriate decisions close to sharp features, not to smooth edges, or when the sampling density is not uniform, to prevent bias. Rather than resorting to manually‐designed geometric priors, we propose to learn how to make these decisions, using ground‐truth data made from synthetic scenes. For this, we project a discretized Hough space representing normal directions onto a structure amenable to deep learning. The resulting normal estimation method outperforms most of the time the state of the art regarding robustness to outliers, to noise and to point density variation, in the presence of sharp edges, while remaining fast, scaling up to millions of points.
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12983