用AI实时测量物体尺寸，支持复杂形状的精准测量。
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment Anything
- 基于SAM模型分步提取轮廓、骨架并转换2D/3D几何特征。
- 在北达科他州油菜茎上实现毫米级直径测量，误差小于5%。
- 适合农业表型分析与机器人抓取，可自动扩展至高通量场景。
我们提出Measure Anything，一个基于视觉的全面框架，用于对具有圆形截面的物体进行尺寸测量，利用分割任意模型（SAM）。该方法能估计棒状结构（含不同曲率）及具有恒定骨线斜率的一般物体的关键几何特征——包括直径、长度和体积。框架集成分割、掩码处理、骨架构建与二维到三维转换，并提供用户友好的界面。我们在北达科他州农田采集的油菜茎上验证了该框架，这些茎体细长且不均匀，现有方法难以应对。准确测量其直径对评估作物健康与产量至关重要。此应用也展示了该框架的潜力：通过整合关键点检测等智能模型，可扩展至全自动高通量测量。此外，我们还展示了其在机器人抓取中的多功能性，利用提取的几何特征识别最优抓取点。
原文摘要 · Abstract (English)
We present Measure Anything, a comprehensive vision-based framework for dimensional measurement of objects with circular cross-sections, leveraging the Segment Anything Model (SAM). Our approach estimates key geometric features -- including diameter, length, and volume -- for rod-like geometries with varying curvature and general objects with constant skeleton slope. The framework integrates segmentation, mask processing, skeleton construction, and 2D-3D transformation, packaged in a user-friendly interface. We validate our framework by estimating the diameters of Canola stems -- collected from agricultural fields in North Dakota -- which are thin and non-uniform, posing challenges for existing methods. Measuring its diameters is critical, as it is a phenotypic traits that correlates with the health and yield of Canola crops. This application also exemplifies the potential of Measure Anything, where integrating intelligent models -- such as keypoint detection -- extends its scalability to fully automate the measurement process for high-throughput applications. Furthermore, we showcase its versatility in robotic grasping, leveraging extracted geometric features to identify optimal grasp points.
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