As a vital aspect of three-dimensional perception in autonomous driving, objection detection leveraging point cloud data has garnered significant attention in recent years. Efficient real-time feature representation is crucial for 3D object detection with this data format. Currently, high-performing detectors offer high accuracy due to the three-dimensional voxels. However, they often fall short in terms of computational efficiency and real-time performance, limiting their practicality in real-world applications. In contrast, detectors with pillar-based structures are more efficient, require fewer computational resources, and are easier to deploy, thereby meeting the demands of real-time applications. Unfortunately, their accuracy does not yet reach that of grid-based methods. In this paper, we present a lightweight and effective pillar-based 3D single-stage object detector named ‘Spatial Inception Pillars’ (SIP), achieving an impressive balance between accu-racy and efficiency. It consists of four parts: a powerful pillar-based feature extraction encoder, the spatial feature extraction backbone network, a neck network that expands the receptive field of multi-scale features, and a versatile detection head. Specifically, in the backbone network, we introduce the Sparse Squeeze-and-Excitation(SE) network in the basic feature blocks for spatial feature enhancement and incorporate a model scaling factor to adjust the model's depth and width for different environmental needs. We propose a receptive field enhancement module based on the basic inception network architecture in the neck network. The network architecture utilizes multi-scale feature information at different stages, achieving high-performance detection. It is an elegant and efficient framework that does not require overly complex calculations and is easy to deploy. Extensive experiments on the large-scale Waymo Open Dataset and nuScenes Dataset demonstrate that SIP surpasses baselines by a large margin and achieves competitive performance with real-time inference speed.


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    Title :

    Spatial Inception Pillars: Enhancing Perceptual Robustness for 3D Object Detection


    Contributors:
    Yang, Xiao (author) / Zhao, Rui (author) / Zhi, Peng (author) / Zhang, YiChi (author) / Zhou, Qingguo (author) / Liu, Gang (author)


    Publication date :

    2024-09-24


    Size :

    571931 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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