Localization of transparent objects using signed distance functions

Jun Yan; Qing Xia; Yijun Ji; Zhijiang Zhang

doi:10.1117/12.2326963

24 July 2018 Localization of transparent objects using signed distance functions

Jun Yan, Qing Xia, Yijun Ji, Zhijiang Zhang

Proceedings Volume 10827, Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018); 108270U (2018) https://doi.org/10.1117/12.2326963
Event: Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018), 2018, Shanghai, China

Abstract

Transparency reconstruction has been a challenging problem in active 3D reconstruction, due to the abnormal transparency appearance of invalid depth and wrong depth captured by structured light sensor. This paper proposes a novel method to localize and reconstruct transparency in domestic environment with real-time camera tracking. Based on the Sighed Distance Function(SDF), we estimate the camera pose by minimizing residual error of multiple depth images in the voxel grid. We adopt asymmetric voting of invalid depth to curve the transparency in 3D domain. Concerning the wrong depth caused by transparency, we build a local model to investigate the depth oscillation of each voxel between frames. With the fusion of depth data, we can get the point cloud of transparency and achieve a higher-quality reconstruction of an indoor scene simultaneously. We explore a series of experiments using a hand-held sensor. The results validate that our approach can accurately localize the transparent objects and improve their 3D model, and is more robust against the interference of camera dithering and other noise.

Citation Download Citation

Jun Yan, Qing Xia, Yijun Ji, and Zhijiang Zhang "Localization of transparent objects using signed distance functions", Proc. SPIE 10827, Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018), 108270U (24 July 2018); https://doi.org/10.1117/12.2326963

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available