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:2607. 02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.
By Jinwen Wen
arXiv:2607. 16286v1 Announce Type: cross Abstract: The 3D geometry of real-world scene data is often incomplete.
By Yingzhao Jian, Zihao Lin, Hehe Fan
arXiv:2507.20881v3 Announce Type: replace
Abstract: Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted conside...
By Ke Niu, Zeyun Liu, Xue Feng, Heng Li, Naian Xiao, Binghua Su, Qika Lin, Kaize Shi
Dual-pixel (DP) imaging enables metric depth estimation from a single camera using sub-aperture disparity. However, the extremely small effective baseline limits disparity observability, leading to structural degradation and depth failure in textureless, low-contrast, or downsampled regions.
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