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Computer Science > Computer Vision and Pattern Recognition

arXiv:2206.15255 (cs)
[Submitted on 30 Jun 2022]

Title:Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery

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Abstract:Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes in MLPs and optimize shapes and deformations in a learning-based manner. In addition to non-rigid deformations, tool occlusion and poor 3D clues from a single viewpoint are also particular challenges in soft tissue reconstruction. To overcome these difficulties, we present a series of strategies of tool mask-guided ray casting, stereo depth-cueing ray marching and stereo depth-supervised optimization. With experiments on DaVinci robotic surgery videos, our method significantly outperforms the current state-of-the-art reconstruction method for handling various complex non-rigid deformations. To our best knowledge, this is the first work leveraging neural rendering for surgical scene 3D reconstruction with remarkable potential demonstrated. Code is available at:this https URL.
Comments:11 pages, 4 figures, conference
Subjects:Computer Vision and Pattern Recognition (cs.CV)
Cite as:arXiv:2206.15255 [cs.CV]
 (orarXiv:2206.15255v1 [cs.CV] for this version)
 https://doi.org/10.48550/arXiv.2206.15255
arXiv-issued DOI via DataCite

Submission history

From: Yuehao Wang [view email]
[v1] Thu, 30 Jun 2022 13:06:27 UTC (22,368 KB)
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