Import to Mendeley

Material Classification from Time-of-Flight Distortions

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

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


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





Altered driver for Kinect v2 is available at Github.


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