Paper Info
Title
Simultaneous Estimation Of Object Region And Depth In Participating Media Using A Tof Camera
Abstract
Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction techniques gets challenging in participating media such as murky water, fog, or smoke. We have developed a method that uses a continuous-wave time-of-flight (ToF) camera to estimate an object region and depth in participating media simultaneously. The scattered light observed by the camera is saturated, so it does not depend on the scene depth. In addition, received signals bouncing off distant points are negligible due to light attenuation, and thus the observation of such a point contains only a scattering component. These phenomena enable us to estimate the scattering component in an object region from a background that only contains the scattering component. The problem is formulated as robust estimation where the object region is regarded as outliers, and it enables the simultaneous estimation of an object region and depth on the basis of an iteratively reweighted least squares (IRLS) optimization scheme. We demonstrate the effectiveness of the proposed method using captured images from a ToF camera in real foggy scenes and evaluate the applicability with synthesized data.
Year
DOI
Venue
2020
10.1587/transinf.2019EDP7219
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
time-of-flight, depth estimation, participating media, light scattering, iteratively reweighted least squares
Computer vision,Computer science,Artificial intelligence
Journal
Volume
Issue
ISSN
E103D
3
1745-1361
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Yuki Fujimura102.37
Motoharu Sonogashira211.37
Masaaki Iiyama31714.23