MultiViPerFrOG: A Globally Optimized Multi-Viewpoint Perception Framework for Camera Motion and Tissue Deformation

TL;DR

This paper introduces MultiViPerFrOG, a robust multi-viewpoint framework that jointly estimates camera motion and tissue deformation, improving 3D reconstruction in surgical environments from moving depth cameras.

Contribution

It presents a novel global optimization framework that integrates perception modules with priors to accurately estimate multiple camera motions and scene flow in deformable scenes.

Findings

Successfully constrains convergence to a unique solution with simulated noisy data

Robust to noisy inputs and processes hundreds of points in milliseconds

Facilitates advanced surgical scene representations for computer-assisted surgery

Abstract

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.