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An Evaluation Approach for Scene Flow with Decoupled Motion and Position

机译:运动与位置解耦的场景流评估方法

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摘要

This chapter presents a technique for estimating the three-dimensional displacement vector field that describes the motion of each visible scene point. This displacement field consists of the change in image position, also known as optical flow, and the change in disparity and is called the scene flow. The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the disparity (3D position) and the scene flow (optical flow and disparity change) estimation steps. Thus we present a technique to estimate a dense scene flow field using a variational approach from the gray value images and a given stereo disparity map.rnAlthough the two subproblems disparity and scene flow estimation are decoupled, we enforce the scene flow to yield consistent displacement vectors in all four stereo images involved at two time instances. The decoupling strategy has two benefits: Firstly, we are independent in choosing a disparity estimation technique, which can yield either sparse or dense correspondences, and secondly, we can achieve frame rates of 5 fps on standard consumer hardware. This approach is then expanded to real-world displacements, and two metrics are presented that define likelihoods of movement with respect to the background. Furthermore, an evaluation approach is presented to compare scene flow algorithms on long image sequences, using synthetic data as ground truth.
机译:本章介绍一种估计三维位移矢量场的技术,该技术描述了每个可见场景点的运动。该位移场由图像位置的变化(也称为光流)和视差的变化组成,称为场景流。提出的技术使用了来自立体声序列的两个连续的图像对。主要作用是使视差(3D位置)与场景流(光学流和视差变化)估计步骤分离。因此,我们提出了一种使用灰度值图像和给定立体视差图的变分方法来估计密集场景流场的技术.rn尽管两个子问题视差和场景流估计是分离的,但我们强制场景流以产生一致的位移矢量在两个时间实例中涉及的所有四个立体图像中。去耦策略有两个好处:首先,我们独立选择视差估计技术,该技术可以产生稀疏或密集的对应关系;其次,我们可以在标准消费类硬件上实现5 fps的帧速率。然后将这种方法扩展到现实世界中的位移,并提出了两个度量标准,这些度量标准定义了相对于背景运动的可能性。此外,提出了一种评估方法,以合成图像为基础,比较长图像序列上的场景流算法。

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