Robotic Arm Platform for Multi-View Image Acquisition and 3D Reconstruction in Minimally Invasive Surgery

Abstract

Minimally invasive surgery (MIS) offers significant benefits, such as reduced recovery time and minimised patient trauma, but poses challenges in visibility and access, making accurate 3D reconstruction a significant tool in surgical planning and navigation. This work introduces a robotic arm platform for efficient multi-view image acquisition and precise 3D reconstruction in MIS settings. We adapted a laparoscope to a robotic arm and captured ex-vivo images of several ovine organs across varying lighting conditions (operating room and laparoscopic) and trajectories (spherical and laparoscopic). We employed recently released learning-based feature matchers combined with COLMAP to produce our reconstructions. The reconstructions were evaluated against high-precision laser scans for quantitative evaluation. Our results show that whilst reconstructions suffer most under realistic MIS lighting and trajectory, two matching methods achieve close to sub-millimetre accuracy with 0.80 and 0.76 mm Chamfer distances and 1.06 and 0.98 mm RMSEs for ALIKED and GIM respectively. Our best reconstruction results occur with operating room lighting and spherical trajectories. Our robotic platform provides a tool for controlled, repeatable multi-view data acquisition for 3D generation in MIS environments, which can lead to new datasets necessary for novel learning-based surgical models.