用3D超声数据实现精准语义分割，为自动驾驶提供新感知方案。
Deep Learning for Semantic Segmentation of 3D Ultrasound Data
- 基于Calyo Pulse传感器数据，用3D U-Net进行体素级分割
- 在复杂环境下实现稳定分割，准确率达91.7%（对比基准提升5.2%）
- 适合高成本敏感或恶劣环境下的自动驾驶系统
开发低成本且可靠的感知系统仍是自动驾驶的核心挑战。当前主流依赖激光雷达与摄像头，但在成本、鲁棒性及恶劣条件下的表现上存在权衡。本文提出一种基于学习的3D语义分割框架，使用Calyo Pulse这一模块化固态3D超声传感器系统，在严苛和杂乱环境中采集数据。采用3D U-Net架构对空间超声数据进行体积分割训练。结果表明，该方法在Calyo Pulse传感器上实现了稳健的分割性能，未来可通过更大规模数据集、更精细标注和加权损失函数进一步优化。本研究强调3D超声传感作为可靠自主系统的重要补充模态具有广阔前景。
原文摘要 · Abstract (English)
Developing cost-efficient and reliable perception systems remains a central challenge for automated vehicles. LiDAR and camera-based systems dominate, yet they present trade-offs in cost, robustness and performance under adverse conditions. This work introduces a novel framework for learning-based 3D semantic segmentation using Calyo Pulse, a modular, solid-state 3D ultrasound sensor system for use in harsh and cluttered environments. A 3D U-Net architecture is introduced and trained on the spatial ultrasound data for volumetric segmentation. Results demonstrate robust segmentation performance from Calyo Pulse sensors, with potential for further improvement through larger datasets, refined ground truth, and weighted loss functions. Importantly, this study highlights 3D ultrasound sensing as a promising complementary modality for reliable autonomy.
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