Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment Anything
This work addresses the need for automated measurement of phenotypic traits in agriculture, specifically for Canola crop health and yield, though it is incremental as it builds on existing segmentation models like SAM.
The paper tackles the problem of real-time dimensional measurement of objects with circular cross-sections, such as Canola stems, by developing a vision-based framework that estimates diameters, lengths, and volumes, achieving validation on thin, non-uniform stems with critical agricultural applications.
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.