Non-Lambertian Surfaces and Their Challenges for Visual SLAM

被引:0
|
作者
Pyykola, Sara [1 ]
Joswig, Niclas [1 ]
Ruotsalainen, Laura [1 ]
机构
[1] Univ Helsinki, Fac Sci, Dept Comp Sci, PL 64, Helsinki, Finland
关键词
Reflection; Reflectivity; Computer vision; Vectors; Mathematical models; Simultaneous localization and mapping; Three-dimensional displays; Monocular depth estimation; navigation; computer vision; non-Lambertian surfaces; specularities; PHOTOMETRIC STEREO; SIMULTANEOUS LOCALIZATION; REFLECTANCE; ILLUMINATION; PERCEPTION; SHAPE;
D O I
10.1109/OJCS.2024.3419832
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities, which pose a potential issue in industrial SLAM. This article reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ResNeXt-101-WSL, ORB SLAM 3 and TartanVO is performed on the data. The article concludes with discussion about the results of empirical evaluation and the findings of the survey.
引用
收藏
页码:430 / 445
页数:16
相关论文
共 50 条
  • [41] RADIATION PHYSICS AND MODELING FOR OFF-NADIR SATELLITE-SENSING OF NON-LAMBERTIAN SURFACES
    GERSTL, SAW
    SIMMER, C
    REMOTE SENSING OF ENVIRONMENT, 1986, 20 (01) : 1 - 29
  • [42] Is the optical image of a non-Lambertian fractal surface fractal?
    Korvin, G
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2005, 2 (04) : 380 - 383
  • [43] Comparison of Lambertian and non-Lambertian topographic normalization algorithms: A case study in Gelibolu, Turkey
    Cetin, M
    Musaoglu, N
    GEOINFORMATION FOR EUROPEAN-WIDE INTEGRATION, 2003, : 113 - 117
  • [44] Visible Light Positioning With Lens Compensation for Non-Lambertian Emission
    Meunier, Ben
    Cosmas, John
    Ali, Kareem
    Jawad, Nawar
    Eappen, Geoffrey
    Zhang, Xun
    Zhao, Hongxiu
    Li, Wei
    Zhang, Hequn
    IEEE TRANSACTIONS ON BROADCASTING, 2023, 69 (01) : 289 - 302
  • [45] A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
    Shi, Boxin
    Mo, Zhipeng
    Wu, Zhe
    Duan, Dinglong
    Yeung, Sai-Kit
    Tan, Ping
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2019, 41 (02) : 271 - 284
  • [46] Multiscale Convolutional Fusion Network for Non-Lambertian Photometric Stereo
    Ren, Jieji
    Wang, Xi
    Jian, Zhenxiong
    Ren, Mingjun
    IEEE SIGNAL PROCESSING LETTERS, 2020, 27 : 1929 - 1933
  • [47] 3D Tensor Display for Non-Lambertian Content
    Losfeld, Armand
    Soetens, Eline
    Bonatto, Daniele
    Fachada, Sarah
    Van Bogaert, Laurie
    Lafruit, Gauthier
    Teratani, Mehrdad
    2022 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2022,
  • [48] Surface orientation imager with excluding capability of non-Lambertian reflectance
    Kurihara, T
    Shimizu, T
    Ono, N
    Ando, S
    Videometrics VIII, 2005, 5665 : 9 - 16
  • [49] Model for analyzing visual images of non-Lambertian objects observed through a light-scattering medium
    Barun, VV
    17TH CONGRESS OF THE INTERNATIONAL COMMISSION FOR OPTICS: OPTICS FOR SCIENCE AND NEW TECHNOLOGY, PTS 1 AND 2, 1996, 2778 : 111 - 112
  • [50] A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
    Shi, Boxin
    Wu, Zhe
    Mo, Zhipeng
    Duan, Dinglong
    Yeung, Sai-Kit
    Tan, Ping
    2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 3707 - 3716