2018/09/28 by Seongjong Song, Song, Seongjong, Hyunjung Shim +1 · 1 citation
Computer Science · Environmental Science · Physics and Astronomy · #Advanced Optical Sensing Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Optical measurement and interference techniques #Remote Sensing and LiDAR Applications
paper · pdf · doi:10.48550/arxiv.1809.10917
openalex publication_date 2018/09/28 · openalex created_date 2018/10/05 · openalex updated_date 2026/07/28
We propose a novel approach to recovering the translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks. When recording the translucent objects using the ToF depth camera, their depth values are severely contaminated due to complex light interactions with the 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 the 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.