In autonomous driving systems, the monocular 3D object detection algorithm is a crucial component. The safety of autonomous vehicles heavily depends on a well-designed detection system. Therefore, developing a robust and efficient 3D object detection algorithm is a major goal for institutes and researchers. Having a 3D sense is essential in autonomous vehicles and robotics, as it allows the system to understand its surroundings and react accordingly. Compared with stereo-based and Lidar-based methods, monocular 3D Object detection is a challenging task as it only utilizes 2D information to generate complex 3D features, making it low-cost, less computationally intensive, and with great potential. However, the performance of monocular methods is impaired due to the lack of depth information. In this paper, we propose a simple, end-to-end, and effective network for monocular 3D object detection without the use of external training data. Our work is inspired by auxiliary learning, in which we use a robust feature extractor as our backbone and multiple regression heads to learn auxiliary knowledge. These auxiliary regression heads will be discarded after training for improved inference efficiency, allowing us to take advantage of auxiliary learning and enabling the model to learn critical information more conceptually. The proposed method achieves 17.28% and 20.10% for the moderate level of the Car category on the KITTI benchmark test set and validation set, respectively, which outperforms the previous monocular 3D object detection approaches.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Monocular 3D Object Detection Utilizing Auxiliary Learning With Deformable Convolution


    Contributors:


    Publication date :

    2024-03-01


    Size :

    2024684 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Boosting Monocular 3D Object Detection With Object-Centric Auxiliary Depth Supervision

    Kim, Youngseok / Kim, Sanmin / Sim, Sangmin et al. | IEEE | 2023



    One Stage Monocular 3D Object Detection Utilizing Discrete Depth and Orientation Representation

    Haq, Muhamad Amirul / Ruan, Shanq-Jang / Shao, Mei-En et al. | IEEE | 2022


    Deformable Surface 3D Reconstruction from Monocular Images

    Salzmann, Matthieu / Fua, Pascal | TIBKAT | 2010


    Efficient Active Learning Strategies for Monocular 3D Object Detection

    Hekimoglu, Aral / Schmidt, Michael / Marcos-Ramiro, Alvaro et al. | IEEE | 2022