3D object detection is a well-known problem for autonomous systems. Most of the existing methods use sensor fusion techniques with Radar, LiDAR, and Cameras. However, one of the challenges is to estimate the 3D shape and location of the adjoining vehicles from a single monocular image without other 3D sensors; such as Radar or LiDAR. To solve the lack of the depth information, a novel method for 3D vehicle detection is presented. In this work, instead of using the whole depth map and the viewing angle (allocentric angle), only the depth mask of each object is used to refine the projected centroid and estimate its egocentric angle directly. The performance of the proposed method is tested and validated using the KITTI dataset, obtaining similar results to other state-of-the-art methods for Monocular 3D Object Detection.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Mono-DCNet: Monocular 3D Object Detection via Depth-based Centroid Refinement and Pose Estimation


    Contributors:


    Publication date :

    2022-06-05


    Size :

    3128507 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Efficient Monocular Coarse-to-Fine Object Pose Estimation

    Feng, Rong / Zhang, Hong | British Library Conference Proceedings | 2016


    Pose estimation for monocular image object using convolution neural network

    Li, Hangyu / Wu, Han / Zhang, Zhilong et al. | IEEE | 2021



    Mono-LSDE: Lightweight Semantic-CNN for Depth Estimation from Monocular Aerial Images*

    Astudillo, Armando / Al-Kaff, Abdulla / Madridano, Angel et al. | IEEE | 2020


    SYSTEM AND METHOD TO IMPROVE MULTI-CAMERA MONOCULAR DEPTH ESTIMATION USING POSE AVERAGING

    GUIZILINI VITOR / AMBRUS RARES ANDREI / GAIDON ADRIEN DAVID et al. | European Patent Office | 2023

    Free access