Real-time scene reconstruction and triangle mesh generation using multiple RGB-D cameras

We present a novel 3D reconstruction system that can generate a stable triangle mesh using data from multiple RGB-D sensors in real time for dynamic scenes. The first part of the system uses moving least squares (MLS) point set surfaces to smooth and filter point clouds acquired from RGB-D sensors....

Full description

Saved in:
Bibliographic Details
Published in:Journal of Real-Time Image Processing Vol. 16; no. 6; pp. 2247 - 2259
Main Authors: Meerits, Siim, Nozick, Vincent, Saito, Hideo
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Science and Business Media LLC 01.12.2019
Springer Berlin Heidelberg
Springer Nature B.V
Springer Verlag
Subjects:
3D reconstruction
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Algorithms
Cameras
Computer Graphics
Computer Science
Computer Vision and Pattern Recognition
Geometry
GPU
Graphics
Image Processing
Image Processing and Computer Vision
Mesh generation
mesh zippering
meshing
Methods
Multimedia Information Systems
Original Research Paper
Pattern Recognition
Real time
Reconstruction
RGB-D cameras
Sensors
Signal,Image and Speech Processing
3D reconstruction
Mesh zippering
Meshing
GPU
RGB-D cameras
mesh zippering
meshing
ISSN:1861-8200, 1861-8219
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present a novel 3D reconstruction system that can generate a stable triangle mesh using data from multiple RGB-D sensors in real time for dynamic scenes. The first part of the system uses moving least squares (MLS) point set surfaces to smooth and filter point clouds acquired from RGB-D sensors. The second part of the system generates triangle meshes from point clouds. The whole pipeline is executed on the GPU and is tailored to scale linearly with the size of the input data. Our contributions include changes to the MLS method for improving meshing, a fast triangle mesh generation method and GPU implementations of all parts of the pipeline.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-017-0736-x