arXiv Computer Vision
Colon3R: Cross-Domain 3D Reconstruction from Monocular Colonoscopic Video
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Sep 21
Colon3R: Cross-Domain 3D Reconstruction from Monocular Colonoscopic Video
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...
arXiv Computer Vision
Aug 26
C3VDReg: A Benchmark for Local-to-Local Colonoscopic Registration toward Anatomical Localization
C3VDReg is a benchmark for local-to-local colonoscopic registration that uses the Colonoscopy 3D Video Dataset (C3VD) to generate 10,015 partial-to-partial point cloud pairs, with 2,088 held‑out test pairs. Each pair consists of a source point cloud from depth reprojection and a target point cloud from CT mesh raycasting, evaluated under a standardized protocol of 8,192 points per cloud and fixed pose conventions. Experiments show that high geometric overlap does not guarantee reliable pose recovery, revealing translation ambiguity along repetitive tubular anatomy as a key failure mode.
arXiv AI
Jun 17
Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation
arXiv:2606. 17340v1 Announce Type: cross Abstract: Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substantial appearance variation across domains, all of which complicate pose estimation, depth prediction, and image-to-anatomy alignment.
Hugging Face Trending Papers
Jul 13
FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry
We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fidelity, affine-invariant geometry model by initializing with DINOv3 and training on a curated 10.
arXiv Computer Vision
Sep 16
Lesion-centered 3D mapping of colonoscopy procedures: validation of a hierarchical ensemble pipeline on public benchmark videos
The paper presents a four‑layer hierarchical pipeline that constructs a lesion‑centered spatial record from colonoscopy videos without full‑colon 3D reconstruction. It combines a global topological map, lesion‑level spatio‑temporal tracks, on‑demand local 3D reconstruction, and persistent lesion identity across repeated observations, and evaluates the system on four public videos. The results show successful detection of revisit events, accurate lesion identity merging, and superior geometry accuracy compared to a general‑purpose foundation model.
arXiv Computer Vision
Sep 3
MV-dVRK: A Multi-Viewpoint Benchmark for Spatial Surgical Perception
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.