Network-based structure flow estimation

Date

2020

Authors

Liu, Shu
Barnes, Nick
Mahony, Robert
Ye, Haolei

Journal Title

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Volume Title

Publisher

IEEE

Abstract

Structure flow is a novel three-dimensional motion representation that differs from scene flow in that it is directly associated with image change. Due to its close connection with both optical flow and divergence in images, it is well suited to estimation from monocular vision. To acquire an accurate measurement of structure flow, we design a method that employs the spatial pyramid structure and the network-based method. We investigate the current motion field datasets and validate the performance of our method by comparing its two-dimensional component of motion field with the previous works. In general, we experimentally show two conclusions: 1. Our motion estimator employs only RGB images and outperforms the previous work that utilizes RGB-D images. 2. The estimated structure flow map is a more effective representation for demonstrating the motion field compared with the widely-accepted scene flow via monocular vision.

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Citation

Source

Type

Conference paper

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Access Statement

License Rights

DOI

10.1109/DICTA51227.2020.9363398

Restricted until

2099-12-31