학술논문

Numerically stable multi-channel depth scene flow with adaptive weighting of regularization terms
Document Type
Conference
Source
2020 28th European Signal Processing Conference (EUSIPCO) European Signal Processing Conference (EUSIPCO), 2020 28th. :605-609 Jan, 2021
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Signal Processing and Analysis
Video coding
Three-dimensional displays
Smoothing methods
Estimation
Signal processing
Numerical stability
Videos
numerical stability
scene flow
RGB-D
variational method
multi-channel
Language
ISSN
2076-1465
Abstract
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.