DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology
2026-07-14 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors focus on improving how doctors measure and understand skin lesions by creating 3D models from just one image, without needing special equipment. They developed DermDepth, a tool that builds accurate 3D skin images with real-world size scales, and D-Synth, a synthetic dataset with perfect 3D details to help train the system. Their experiments show that training with D-Synth greatly improves measurement accuracy on real images. After refining the tool with a small set of real cases, DermDepth works well across different skin types and wound sizes, matching medical standards. The work helps move skin analysis beyond flat pictures to more precise 3D understanding.
dermatology3D reconstructionmonocular depth estimationdermoscopysynthetic datasetmetric scalesurface normalswound measurementskin lesion tracking
Authors
Héctor Carrión, Narges Norouzi
Abstract
Dermatological practice routinely involves measuring and tracking lesion size, morphology and texture, as critical components of wound or skin cancer screening, monitoring and diagnosis. To accomplish this task, practitioners often image the skin surface with commonly available off-the-shelf camera sensors. This has led to an overwhelming research focus on 2D methods while these objectives naturally benefit from 3D information. In this paper, we demonstrate that dense monocular 3D reconstructions, metric scale measurements and rich surface normal texture estimates are achievable for both dermoscopic and macroscopic cases without the need for additional hardware or multiple captures. We present DermDepth, the first single-view metric scale 3D model for the dermatological domain and D-Synth, the first synthetic dermoscopic dataset with pixel-perfect 3D information. Our experiments show training DermDepth on D-Synth corrects metric scale error from over 16x to under 1.1x for real dermoscopic data, while preserving geometric quality and increasing texture richness. Fine-tuning on a small amount of real clinical samples generalizes our method across three real-world benchmarks spanning the few mm to hundred cm range, diverse skin-tones, chronic wound cases and produces measurements broadly consistent with disease size reported in medical literature. All code, data and models are available at https://github.com/hectorcarrion/dermdepth.