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Camera-Based 3D Semantic Scene Completion With Sparse Guidance Network.

Jianbiao Mei, Yu Yang, Mengmeng Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 26, 2024
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    Summary

    This study introduces SGN, a novel one-stage framework for camera-based semantic scene completion (SSC) in autonomous driving. SGN effectively propagates semantic information using spatial geometry cues, achieving superior performance and efficiency.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Semantic Scene Completion (SSC) is crucial for autonomous driving, predicting 3D scene occupancy from limited observations.
    • Camera-based SSC methods offer advantages but struggle with discriminative segmentation due to heavy 3D models.
    • Existing approaches lack sufficient feature separation and efficient semantic propagation.

    Purpose of the Study:

    • To propose SGN, a one-stage, camera-based SSC framework using a dense-sparse-dense design.
    • To enhance semantic propagation using spatial geometry cues and a novel sparse voxel proposal network.
    • To improve feature separation and accelerate convergence for accurate 3D scene understanding.

    Main Methods:

    • Redesigned sparse voxel proposal network for depth-aware context and coarse-to-fine processing.
    • Implemented hybrid guidance (sparse semantic and geometry) and effective voxel aggregation.
    • Developed a multi-scale semantic propagation module for flexible receptive fields and reduced computation.

    Main Results:

    • SGN demonstrates superior performance on SemanticKITTI and SSCBench-KITTI-360 datasets.
    • The lightweight SGN-L achieves 14.80% mIoU and 45.45% IoU on SemanticKITTI validation.
    • SGN-L utilizes only 12.5M parameters and 7.16G training memory, showcasing efficiency.

    Conclusions:

    • SGN offers a highly effective and efficient solution for camera-based semantic scene completion.
    • The proposed methods significantly improve feature separation and semantic propagation accuracy.
    • SGN provides a strong baseline for future research in autonomous driving perception.