We propose a novel approach to recovering translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks. When recording translucent objects using the ToF depth camera, their depth values are severely contaminated due to complex light interactions with surrounding environment. While existing methods suggested new capture systems or developed the depth distortion models, their solutions were less practical because of strict assumptions or heavy computational complexity. In this paper, we adopt deep residual networks for modeling the ToF depth distortion caused by translucency. To fully utilize both the local and semantic information of objects, multi-scale patches are used to predict the depth value. Based on the quantitative and qualitative evaluation on our benchmark database, we show the effectiveness and robustness of the proposed algorithm.
|Title of host publication||Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers|
|Editors||Hongdong Li, Konrad Schindler, C.V. Jawahar, Greg Mori|
|Number of pages||17|
|Publication status||Published - 2019|
|Event||14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia|
Duration: 2018 Dec 2 → 2018 Dec 6
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th Asian Conference on Computer Vision, ACCV 2018|
|Period||18/12/2 → 18/12/6|
Bibliographical noteFunding Information:
Acknowledgement. This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the University Information Technology Research Center support program (IITP-2016-R2718-16-0014) supervised by the IITP, by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIP (NRF-2016R1A2B4016236), and also by the MIST(Ministry of Science and ICT), Korea, under the “ICT Consilience Creative Program” (IITP-2018-2017-0-01015) supervised by the IITP(Institute for Information & communications Technology Promotion).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)