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3D-MSFC: A 3D multi-scale features compression method for object detection☆
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AI
DOI：10.1016/j.displa.2024.102880.png)
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Abstract
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As machine vision tasks rapidly evolve, anew concept of compression, namely video coding for machines (VCM), has emerged. However, current VCM methods are only suitable for 2D machine vision tasks. With the popularization of autonomous driving, the demand for 3D machine vision tasks has significantly increased, leading to an explosive growth in LiDAR data that requires efficient transmission. To address this need, we propose a machine vision-based point cloud coding paradigm inspired by VCM. Specifically, we introduce a 3D multi-scale features compression (3D-MSFC) method, tailored for 3D object detection. Experimental results demonstrate that 3D-MSFC achieves less than a 3% degradation in object detection accuracy at a compression ratio of 2796x. Furthermore, its low-profile variant, 3D-MSFC-L, achieves less than a 2% degradation in accuracy at a compression ratio of 463x. The above results indicate that our proposed method can provide an ultra-high compression ratio while ensuring no significant drop inaccuracy, greatly reducing the amount of data required for transmission during each detection. This can significantly lower bandwidth consumption and save substantial costs in application scenarios such as smart cities.
Keywords:
Machine vision-based point cloud coding
3D multi-scale features compression
3D object detection
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