Wound3DAssist is a practical framework that creates 3D wound models from short handheld videos taken with consumer‑grade devices, enabling non‑contact, automatic measurements of wound surfaces. The system integrates 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. Evaluations on digital models, silicone phantoms, and real patients show millimeter‑scale reconstruction accuracy and multi‑view tissue composition analysis, with full assessments completed in under 20 minutes.
By Remi Chierchia, Rodrigo Santa Cruz, L\'eo Lebrat, Yulia Arzhaeva, Mohammad Ali Armin, Jeremy Oorloff, Chuong Nguyen, Olivier Salvado, Clinton Fookes, David Ahmedt-Aristizabal
Monocular colonoscopic 3D reconstruction is important for surgical robotic colonoscopy, but remains challenging due to weak texture, specular reflections, limited view overlap, and non-rigid tissue mo...
MV-dVRK is the first ex‑vivo surgical dataset that provides multiple exposure‑synchronized stereo viewpoints, accurate surface geometry, and ground‑truth camera poses for endoscopic images. The benchmark’s static subset offers dense SfM reference geometry validated against an industrial 3D scanner, while the dynamic sequences cover ten surgical tasks with increasing kinematic complexity and tissue deformation. Using MV‑dVRK, the authors systematically compare zero‑shot monocular, stereo, multi‑stereo, and multi‑view 3D reconstruction methods, finding that multi‑stereo reconstruction with two endoscopes yields the highest coverage, and that optimization‑based multi‑view methods outperform feed‑forward foundation models when a third viewpoint is added.
By Guido Caccianiga, Sergey Prokudin, Yutong Chen, Bernard Javot, Rachael L'Orsa, Omer Burak Alada\u{g}, Yarden Sharon, Jens Rolinger, Ivan Capobianco, Anton Deguet, Siyu Tang, Katherine J. Kuchenbecker
arXiv:2609.23961v1 Announce Type: new
Abstract: Monocular colonoscopic 3D reconstruction is important for surgical robotic colonoscopy, but remains challenging due to weak texture, specular reflectio...
By Zhihao Xing, Yingyu Wang, Liang Zhao, Shoudong Huang
3D Morphable Models (3DMMs) remain the standard parametric shape priors for many state-of-the-art 3D face reconstruction algorithms. However, as these models are derived from a finite number of 3D face samples, they inherit the morphological biases of their training data, potentially limiting their generalizability across diverse global populations.
The paper introduces the Semantic Tri-view Pipeline, an interpretable system that automatically screens teledermatology photographs for gradability by analyzing epidermal micro-relief across up to three smartphone views. It uses a lightweight DeepLabV3+ model to segment micro-relief fidelity and aggregates the resulting spatial masks with logistic regression, leveraging viewpoint redundancy to improve robustness. Evaluated on the SCIN dataset, the approach raises the AUC from 0.81 to 0.96 on optically clear cases, offering real‑time, privacy‑by‑design feedback to filter ungradable photo sets before clinician review.
By Robert Engel