Import to Mendeley

Material Classification using Frequency- and Depth-dependent Time-of-Flight Distortion

Kenichiro Tanaka1, Yasuhiro Mukaigawa1, Takuya Funatomi1, Hiroyuki Kubo1, Yasuyuki Matsushita2, Yasushi Yagi2
1 Nara Institute of Science and Technology, 2 Osaka University

CVPR 2017 : [Paper, Open access (PDF)], IEEE Xplore, [bibtex]


  This paper presents a material classification method using an off-the-shelf Time-of-Flight (ToF) camera. We use a key observation that the depth measurement by a ToF camera is distorted in objects with certain materials, especially with translucent materials. We show that this distortion is caused by the variations of time domain impulse responses across materials and also by the measurement mechanism of the existing ToF cameras. Specifically, we reveal that the amount of distortion varies according to the modulation frequency of the ToF camera, the material of the object, and the distance between the camera and object. Our method uses the depth distortion of ToF measurements as features and achieves material classification of a scene. Effectiveness of the proposed method is demonstrated by numerical evaluation and real-world experiments, showing its capability of even classifying visually similar objects.





Altered driver for Kinect v2 is available at Github.


Dataset is available. [dataset-cvpr17.tar.gz]