How to Measure Volume with Computer Vision
Blog post from Roboflow
Computer vision volume measurement estimates an object’s three-dimensional size from images by segmenting its boundaries, calibrating pixel dimensions against a known real-world reference, and obtaining the third dimension through known height, geometric assumptions, depth sensing, or container fill level. The tutorial describes applications in manufacturing, logistics, agriculture, and packaging, and outlines approaches for regular shapes such as boxes, cylinders, and spheres as well as irregular materials requiring depth maps or depth cameras. Its practical Roboflow Workflows example measures a closed rectangular box from a top-down image using SAM 3 segmentation, a visible 5 × 5 cm blue calibration marker, mask-area measurement, and a Custom Python block. The workflow detects the marker to derive pixels per centimeter, fits a rotated rectangle to the box mask to calculate length and width, applies a configured 5 cm height and 0.2 cm wall thickness, and returns annotated external and internal areas and volumes. Accuracy depends on a near-vertical camera view, limited lens and perspective distortion, a fully visible marker on the same plane as the box, correct assumed dimensions, and reliable segmentation, while depth estimation could later automate height measurement.
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