MultiViPerFrOG：一种面向相机运动与组织变形的全局优化多视点感知框架
MultiViPerFrOG: A Globally Optimized Multi-Viewpoint Perception Framework for Camera Motion and Tissue Deformation
AI总结：
本文提出多视点全局优化框架 MultiViPerFrOG，通过融合低级感知模块输出与运动学及场景先验，联合估计多相机运动与绝对场景流，以鲁棒地解决可变形场景中的三维重建问题。
摘要：
从移动深度相机捕获的信息中重建可变形环境的三维形状，与外科手术高度相关。其根本挑战在于，在完全可变形场景中同时估计相机运动和组织变形是一个病态问题，尤其是仅从单个任意移动的视点进行估计时。当前的解决方案通常针对特定器官，缺乏处理大变形所需的鲁棒性。在此，我们提出一种多视点全局优化框架，该框架能够灵活地将低级感知模块（数据关联、深度和相对场景流）的输出与运动学及场景建模先验相结合，以联合估计多个相机运动和绝对场景流。我们使用模拟的含噪数据展示了三个实际示例，这些示例成功地将收敛约束到唯一解。总体而言，我们的方法对组合的含噪输入测量表现出鲁棒性，并且能够在几毫秒内处理数百个点。MultiViPerFrOG 构建了一个通用的免学习时空编码支架，可以解锁先进的手术场景表示，并将促进未来计算机辅助手术技术的发展。
英文摘要：
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.
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