We present a novel technique for fast and accurate reconstruction of depth images from 3D point clouds acquired in urban and rural driving environments. Our approach focuses entirely on the sparse distance and reflectance measurements generated by a LiDAR sensor. The main contribution of this paper is a combined segmentation and upsampling technique that preserves the important semantical structure of the scene. Data from the point cloud is segmented and projected onto a virtual camera image where a series of image processing steps are applied in order to reconstruct a fully sampled depth image. We achieve this by means of a multilateral filter that is guided into regions of distinct objects in the segmented point cloud. Thus, the gains of the proposed approach are two-fold: measurement noise in the original data is suppressed and missing depth values are reconstructed to arbitrary resolution. Objective evaluation in an automotive application shows state-of-the-art accuracy of our reconstructed depth images. Finally, we show the qualitative value of our images by training and evaluating a RGBD pedestrian detection system. By reinforcing the RGB pixels with our reconstructed depth values in the learning stage, a significant increase in detection rates can be realized while the model complexity remains comparable to the baseline.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds


    Contributors:


    Publication date :

    2017-06-01


    Size :

    325716 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SERALOC: SLAM on semantically annotated radar point-clouds

    Isele, Simon T. / Haas-Fickinger, Fabian / Zollner, J. Marius | IEEE | 2021


    Density-Imbalance-Eased LiDAR Point Cloud Upsampling via Feature Consistency Learning

    Chen, Tso-Yuan / Hsiao, Ching-Chun / Huang, Ching-Chun | IEEE | 2023


    RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds

    Chen, Tzu-Hsuan / Chang, Tian Sheuan | IEEE | 2022


    Image-guided ToF depth upsampling: a survey

    Eichhardt, I. n. | British Library Online Contents | 2017


    Guided depth upsampling for precise mapping of urban environments

    Wirges, Sascha / Roxin, Bjorn / Rehder, Eike et al. | IEEE | 2017