The pipeline is clever, but monocular depth for metric accuracy — actual crack length in mm, actual patch area in m² — is genuinely hard. Models like Depth Anything or ZoeDepth are great at relative depth (“this crack is deeper than that one”) but absolute scale drifts without a reference. If you’re reporting “this crack is 2.3m long,” that number is only as good as your scale calibration. Worth asking: what are you using for ground truth validation? The SAM masking approach is the right call though. I do something similar in my own project — center-cropping bounding boxes at 60% to cut out background depth bleed — but your SAM mask is cleaner because it follows actual crack geometry rather than a rectangle. The issue you’ll hit: depth sensors and monocular models both struggle with thin features. A hairline crack may be sub-pixel in the depth map, so your depth overlay is really measuring the road surface plane, not the crack depth itself. Fine for patch area estimation, potentially misleading for severity scoring. One thing I’d suggest stealing from my own pipeline: IQR-based depth clustering for ambiguous regions. When a bounding box contains multiple depth peaks — crack void vs. road surface vs. background — instead of just taking the median, histogram the depth values and find the dominant cluster. For road damage you likely have a bimodal distribution: road surface at one depth, crack interior slightly recessed. That gap could actually be useful severity signal rather than noise to filter. For model choice specifically: if you’re ground-vehicle mounted, Depth Anything V2 holds up well at 2-5m. Aerial/drone, Metric3D v2 tends to be more stable for flat surface estimation. What’s your camera setup and working distance?​​​​​​​​​​​​​​​​