A tensor voting approach for multi-view 3D scene flow estimation and refinement

Jaesik Park, Tae Hyun Oh, Jiyoung Jung, Yu Wing Tai, In So Kweon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


We introduce a framework to estimate and refine 3D scene flow which connects 3D structures of a scene across different frames. In contrast to previous approaches which compute 3D scene flow that connects depth maps from a stereo image sequence or from a depth camera, our approach takes advantage of full 3D reconstruction which computes the 3D scene flow that connects 3D point clouds from multi-view stereo system. Our approach uses a standard multi-view stereo and optical flow algorithm to compute the initial 3D scene flow. A unique two-stage refinement process regularizes the scene flow direction and magnitude sequentially. The scene flow direction is refined by utilizing 3D neighbor smoothness defined by tensor voting. The magnitude of the scene flow is refined by connecting the implicit surfaces across the consecutive 3D point clouds. Our estimated scene flow is temporally consistent. Our approach is efficient, model free, and it is effective in error corrections and outlier rejections. We tested our approach on both synthetic and real-world datasets. Our experimental results show that our approach out-performs previous algorithms quantitatively on synthetic dataset, and it improves the reconstructed 3D model from the refined 3D point cloud in real-world dataset.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Number of pages15
EditionPART 4
StatePublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: 7 Oct 201213 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume7575 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th European Conference on Computer Vision, ECCV 2012


  • Multi-view
  • Scene Flow
  • Tensor Voting


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