RGBD Camera based Material Recognition via Surface Roughness Estimation

被引:11
|
作者
Kim, Jungjun [1 ]
Lim, Hwasup [2 ]
Ahn, Sang Chul [2 ]
Lee, Seungkyu [1 ]
机构
[1] Kyung Hee Univ, Dept Comp Sci Engn, Seoul, South Korea
[2] KIST, Ctr Imaging Media Res, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
D O I
10.1109/WACV.2018.00217
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Real world objects can be characterized effectively by their shape, color and material types. Material recognition of an arbitrary object at a distance is an important task for the improvement of object recognition, scene understanding, realistic rendering and various virtual and augmented reality applications. Researchers have tried to recognize material types based on color features, however material type of an object is not completely correlated with its visual appearance. In this paper, we propose a simple but effective surface roughness estimation method using single time-of-flight (ToF) camera. A set of features extracted from the estimated roughness together with conventional color features are used for material type recognition. Experimental results on our material data set with 122 subjects show promising material type recognition results.
引用
收藏
页码:1963 / 1971
页数:9
相关论文
共 50 条
  • [21] Robust hand posture recognition based on RGBD images
    Dong, Liang
    Wang, Hongpeng
    Hao, Ziyi
    Liu, Jingtai
    26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC), 2014, : 2735 - 2740
  • [22] Recognition of Surface Roughness Based on Fractal Theory and the Microscopic Images
    Guan, Zhenzhen
    Ye, Minghui
    Yin, Xiaochun
    Luo, Xiaohe
    INDUSTRIAL INSTRUMENTATION AND CONTROL SYSTEMS, PTS 1-4, 2013, 241-244 : 3030 - 3033
  • [23] Estimation of fractal dimension and surface roughness based on material characteristics and cutting conditions in the end milling of carbon steels
    Zuo, Xue
    Zhu, Hua
    Zhou, Yuankai
    Yang, Jianhua
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2017, 231 (08) : 1423 - 1437
  • [24] METHODS OF ESTIMATION SURFACE ROUGHNESS
    Shmatko, A. A.
    2013 INTERNATIONAL KHARKOV SYMPOSIUM ON PHYSICS AND ENGINEERING OF MICROWAVES, MILLIMETER AND SUBMILLIMETER WAVES (MSMW), 2013, : 637 - 639
  • [25] Self-Supervised Learning of Camera-based Drivable Surface Roughness
    Cech, Jan
    Hanis, Tomas
    Konopisky, Adam
    Rurtle, Tomas
    Svancar, Jan
    Twardzik, Tomas
    2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2021, : 1319 - 1325
  • [26] Visual 3D Reconstruction System Based on RGBD Camera
    Zhang, Guangcai
    Yu Jiao
    Wang Zhihao
    Gao Xiuqian
    He Jirong
    PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020), 2020, : 908 - 911
  • [27] Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
    Pandey, Rohit
    Tkach, Anastasia
    Yang, Shuoran
    Pidlypenskyi, Pavel
    Taylor, Jonathan
    Martin-Brualla, Ricardo
    Tagliasacchi, Andrea
    Papandreou, George
    Davidson, Philip
    Keskin, Cem
    Izadi, Shahram
    Fanello, Sean
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 9701 - 9710
  • [28] Object pose and surface material recognition using a single-time-of-flight camera
    Yang, Dongzhao
    An, Dong
    Xu, Tianxu
    Zhang, Yiwen
    Wang, Qiang
    Pan, Zhongqi
    Yue, Yang
    ADVANCED PHOTONICS NEXUS, 2024, 3 (05):
  • [29] Automatic camera pose estimation based on a flat surface map
    Ji, Yonghoon
    Yamashita, Atsushi
    Umeda, Kazunori
    Asama, Hajime
    FOURTEENTH INTERNATIONAL CONFERENCE ON QUALITY CONTROL BY ARTIFICIAL VISION, 2019, 11172
  • [30] Geometry-guided multilevel RGBD fusion for surface normal estimation
    Tong, Yanfeng
    Chen, Jing
    Wang, Yongtian
    COMPUTER COMMUNICATIONS, 2023, 206 : 73 - 84