Accurate dense depth estimation is crucial for autonomous vehicles to analyze their environment. This paper presents a non-deep learning-based approach to densify a sparse LiDAR-based depth map using a guidance RGB image. To achieve this goal the RGB image is at first cleared from most of the camera-LiDAR misalignment artifacts. Afterward, it is over segmented and a plane for each superpixel is approximated. In the case a superpixel is not well represented by a plane, a plane is approximated for a convex hull of the most inlier. Finally, the pinhole camera model is used for the interpolation process and the remaining areas are interpolated. The evaluation of this work is executed using the KITTI depth completion benchmark, which validates the proposed work and shows that it outperforms the state-of-the-art non-deep learning-based methods, in addition to several deep learning-based methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deterministic Guided LiDAR Depth Map Completion


    Contributors:


    Publication date :

    2021-07-11


    Size :

    3619539 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    High-Resolution LiDAR Depth Completion Algorithm Guided by Image Topography Maps

    Xing, Genyuan / Lin, Jun / Wu, Kunyang et al. | IEEE | 2025


    Non-Guided Depth Completion with Adversarial Networks

    Tsuji, Yuki / Chishiro, Hiroyuki / Kato, Shinpei | IEEE | 2018


    LiDAR Depth Completion Using Color-Embedded Information via Knowledge Distillation

    Hwang, Sangwon / Lee, Junhyeop / Kim, Woo Jin et al. | IEEE | 2022


    Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving

    Song, Zhenbo / Lu, Jianfeng / Yao, Yazhou et al. | IEEE | 2022


    LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving

    Fu, Chen / Mertz, Christoph / Dolan, John M. | IEEE | 2019