DEPTH ENHANCEMENT USING RGB-D GUIDED FILTERING

被引:0
|
作者
Hui, Tak-Wai [1 ]
Ngan, King Ngi [1 ]
机构
[1] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
关键词
Depth enhancement; guided image filtering; hole filling; linear regression;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Depth maps from low-cost RGB-D system are generally noisy and not accurate enough. Holes often exist in the depth maps. Bilateral filter is commonly utilized to perform depth enhancement. However, it requires high computational time. Its texture transferring property also makes those boundaries between textured and homogeneous regions in the filtered depth map far from satisfactory. In this paper, we present a method to filter raw depth maps using a RGB-D guided filtering in a two-stage framework. Our method not only has a faster computational time than bilateral filter but also avoids the problem of over-texture transfer. We also use RGB-D frames to fill holes in the depth maps. This can effectively prevents depth bleeding artifacts.
引用
收藏
页码:3832 / 3836
页数:5
相关论文
共 50 条
  • [21] Guided residual network for RGB-D salient object detection with efficient depth feature learning
    Wang, Jian
    Chen, Shuhan
    Lv, Xiao
    Xu, Xiuqi
    Hu, Xuelong
    VISUAL COMPUTER, 2022, 38 (05): : 1803 - 1814
  • [22] RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention
    Qin, Ran
    Ma, Haoxiang
    Ciao, Boyang
    Huang, Di
    2023 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2023), 2023, : 8003 - 8009
  • [23] Structure Selective Depth Superresolution for RGB-D Cameras
    Kim, Youngjung
    Ham, Bumsub
    Oh, Changjae
    Sohn, Kwanghoon
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2016, 25 (11) : 5227 - 5238
  • [24] Guided residual network for RGB-D salient object detection with efficient depth feature learning
    Jian Wang
    Shuhan Chen
    Xiao Lv
    Xiuqi Xu
    Xuelong Hu
    The Visual Computer, 2022, 38 : 1803 - 1814
  • [25] RGB×D: Learning depth-weighted RGB patches for RGB-D indoor semantic segmentation
    Cao, Jinming
    Leng, Hanchao
    Cohen-Or, Daniel
    Lischinski, Dani
    Chen, Ying
    Tu, Changhe
    Li, Yangyan
    Neurocomputing, 2021, 462 : 568 - 580
  • [26] Unsupervised Depth Completion and Denoising for RGB-D Sensors
    Fan, Lei
    Li, Yunxuan
    Jiang, Chen
    Wu, Ying
    2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ICRA 2022, 2022, : 8734 - 8740
  • [27] Shape Preserving RGB-D Depth Map Restoration
    Liu, Wei
    Xue, Haoyang
    Gu, Yun
    Yang, Jie
    Wu, Qiang
    Jia, Zhenhong
    NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III, 2014, 8836 : 150 - 158
  • [28] Genetic Algorithm for Depth Images in RGB-D Cameras
    Danciu, Gabriel
    Szekely, Iuliu
    2014 IEEE 20TH INTERNATIONAL SYMPOSIUM FOR DESIGN AND TECHNOLOGY IN ELECTRONIC PACKAGING (SIITME), 2014, : 233 - 238
  • [29] Depth-Aware CNN for RGB-D Segmentation
    Wang, Weiyue
    Neumann, Ulrich
    COMPUTER VISION - ECCV 2018, PT XI, 2018, 11215 : 144 - 161
  • [30] Deep RGB-D Saliency Detection Without Depth
    Zhang, Yuan-fang
    Zheng, Jiangbin
    Jia, Wenjing
    Huang, Wenfeng
    Li, Long
    Liu, Nian
    Li, Fei
    He, Xiangjian
    IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 24 : 755 - 767