MultiViPerFrOG: A Globally Optimized Multi-Viewpoint Perception Framework for Camera Motion and Tissue Deformation
<|reference_start|>MultiViPerFrOG: A Globally Optimized Multi-Viewpoint Perception Framework for Camera Motion and Tissue Deformation: Reconstructing the 3D shape of a deformable environment from the information captured by a moving depth camera is highly relevant to surgery. The underlying challenge is the fact that simultaneously estimating camera motion and tissue deformation in a fully deformable scene is an ill-posed problem, especially from a single arbitrarily moving viewpoint. Current solutions are often organ-specific and lack the robustness required to handle large deformations. Here we propose a multi-viewpoint global optimization framework that can flexibly integrate the output of low-level perception modules (data association, depth, and relative scene flow) with kinematic and scene-modeling priors to jointly estimate multiple camera motions and absolute scene flow. We use simulated noisy data to show three practical examples that successfully constrain the convergence to a unique solution. Overall, our method shows robustness to combined noisy input measures and can process hundreds of points in a few milliseconds. MultiViPerFrOG builds a generalized learning-free scaffolding for spatio-temporal encoding that can unlock advanced surgical scene representations and will facilitate the development of the computer-assisted-surgery technologies of the future.<|reference_end|>
@article{caccianiga2024multiviperfrog:,
    title={MultiViPerFrOG: A Globally Optimized Multi-Viewpoint Perception
  Framework for Camera Motion and Tissue Deformation},
    author={Guido Caccianiga, Julian Nubert, Cesar Cadena, Marco Hutter, and
  Katherine J. Kuchenbecker},
    journal={arXiv preprint arXiv:2408.04367},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04367},
    primaryClass={cs.CV}
}