Numerically stable multi-channel depth scene flow with adaptive weighting of regularization terms

Yusuke Kameda, Ichiro Matsuda, Susumu Itoh

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


Scene flow is a three-dimensional (3D) vector field with velocity in the depth direction and optical flow that represents the apparent motion, which can be estimated from RGB-D videos. Scene flow can be used to estimate the 3D motion of objects with a camera; thus, it is used for obstacle detection and self-localization. It can potentially be applied to inter prediction in 3D video coding. The scene-flow estimation method based on the variational method requires numerical computations of nonlinear equations that control the regularization strength to prevent excessive smoothing due to scene-flow regularization. Because numerical stability depends on multi-channel images and computational parameters such as regularization weights, it is difficult to determine appropriate parameters that satisfy the stability requirements. Therefore, we propose a numerical computation method to derive a numerical stability condition that does not depend on the color of the image or the weight of the regularization term. This simplifies the traditional method and facilitates the setting up of various regularization weight functions. Finally, we evaluate the performance of the proposed method.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797053
Publication statusPublished - 24 Jan 2021
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference28th European Signal Processing Conference, EUSIPCO 2020


  • Multi-channel
  • Numerical stability
  • RGB-D
  • Scene flow
  • Variational method


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