article

Multi-View Scene Capture by Surfel Sampling: From Video Streams to Non-Rigid 3D Motion, Shape and Reflectance

Authors:

Rodrigo L. Carceroni,

Kiriakos N. KutulakosAuthors Info & Claims

International Journal of Computer Vision, Volume 49, Issue 2-3

Pages 175 - 214

https://doi.org/10.1023/A:1020145606604

Published: 01 September 2002 Publication History

Abstract

In this paper we study the problem of recovering the 3D shape, reflectance, and non-rigid motion properties of a dynamic 3D scene. Because these properties are completely unknown and because the scene's shape and motion may be non-smooth, our approach uses multiple views to build a piecewise-continuous geometric and radiometric representation of the scene's trace in space-time. A basic primitive of this representation is the dynamic surfel, which (1) encodes the instantaneous local shape, reflectance, and motion of a small and bounded region in the scene, and (2) enables accurate prediction of the region's dynamic appearance under known illumination conditions. We show that complete surfel-based reconstructions can be created by repeatedly applying an algorithm called Surfel Sampling that combines sampling and parameter estimation to fit a single surfel to a small, bounded region of space-time. Experimental results with the Phong reflectance model and complex real scenes (clothing, shiny objects, skin) illustrate our method's ability to explain pixels and pixel variations in terms of their underlying causes—shape, reflectance, motion, illumination, and visibility.

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Cited By

Fang SYe RWang WLiu ZWang YWang YChen SWang Y(2024)FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344286125:11(18321-18337)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3442861
Sarkar KMehta DXu WGolyanik VTheobalt C(2020)Neural Re-rendering of Humans from a Single ImageComputer Vision – ECCV 202010.1007/978-3-030-58621-8_35(596-613)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-58621-8_35
Afzal HAouada DMirbach BOttersten B(2018)Full 3D Reconstruction of Non-Rigidly Deforming ObjectsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/317775614:1s(1-23)Online publication date: 26-Mar-2018
https://dl.acm.org/doi/10.1145/3177756
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Index Terms

Multi-View Scene Capture by Surfel Sampling: From Video Streams to Non-Rigid 3D Motion, Shape and Reflectance
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
2. Theory of computation
  1. Randomness, geometry and discrete structures

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cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 49, Issue 2-3

September-October 2002

146 pages

ISSN:0920-5691

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Copyright © Copyright © 2002 Kluwer Academic Publishers.

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Kluwer Academic Publishers

United States

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Published: 01 September 2002

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Fang SYe RWang WLiu ZWang YWang YChen SWang Y(2024)FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344286125:11(18321-18337)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3442861
Sarkar KMehta DXu WGolyanik VTheobalt C(2020)Neural Re-rendering of Humans from a Single ImageComputer Vision – ECCV 202010.1007/978-3-030-58621-8_35(596-613)Online publication date: 23-Aug-2020
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Vogel CSchindler KRoth S(2015)3D Scene Flow Estimation with a Piecewise Rigid Scene ModelInternational Journal of Computer Vision10.1007/s11263-015-0806-0115:1(1-28)Online publication date: 1-Oct-2015
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Harvent JCoudrin BBrèthes LOrteu JDevy M(2013)Multi-view dense 3D modelling of untextured objects from a moving projector-cameras systemMachine Vision and Applications10.1007/s00138-013-0495-z24:8(1645-1659)Online publication date: 1-Nov-2013
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