用点云快速重建变形物体内部结构，30毫秒内完成精准定位。
LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions
- 基于占用网络从单视角点云重建变形体及其内部结构。
- 真实机器人实验中对目标区域穿刺成功率98.9%，延迟低于30毫秒。
- 提供预测置信度与关键特征解释，适合手术等高安全场景。
准确识别变形物体的形状及其内部结构对于需要精确定位的医疗任务（如机器人活检）至关重要。本文提出LUDO，一种用于低延迟理解变形物体的方法。LUDO利用占用网络，仅需单视角点云观测，在30毫秒内重建物体的变形状态及内部结构。该方法可输出预测不确定性，并通过突出输入中的关键特征实现可解释性，这对安全关键应用（如手术）极为重要。我们在真实机器人实验中评估了LUDO，对多种变形物体内的感兴趣区域（ROIs）穿刺成功率达98.9%。与主流基线对比，LUDO在ROI定位精度、训练时间和内存占用方面均表现更优。结果表明，LUDO可在无需变形配准的情况下实现对变形物体的有效交互。
原文摘要 · Abstract (English)
Accurately determining the shape of deformable objects and the location of their internal structures is crucial for medical tasks that require precise targeting, such as robotic biopsies. We introduce LUDO, a method for accurate low-latency understanding of deformable objects. LUDO reconstructs objects in their deformed state, including their internal structures, from a single-view point cloud observation in under 30 ms using occupancy networks. LUDO provides uncertainty estimates for its predictions. Additionally, it provides explainability by highlighting key features in its input observations. Both uncertainty and explainability are important for safety-critical applications such as surgery. We evaluate LUDO in real-world robotic experiments, achieving a success rate of 98.9% for puncturing various regions of interest (ROIs) inside deformable objects. We compare LUDO to a popular baseline and show its superior ROI localization accuracy, training time, and memory requirements. LUDO demonstrates the potential to interact with deformable objects without the need for deformable registration methods.
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