Real-Time Dense Stereo Reconstruction Using Convex Optimisation with a Cost-Volume for Image-Guided Robotic Surgery

Abstract. Reconstructing the depth of stereo-endoscopic scenes is an important step in providing accurate guidance in robotic-assisted mini-mally invasive surgery. Stereo reconstruction has been studied for decades but remains a challenge in endoscopic imaging. Current approaches can easily fail to reconstruct an accurate and smooth 3D model due to tex-tureless tissue appearance in the real surgical scene and occlusion by instruments. To tackle these problems, we propose a dense stereo re-construction algorithm using convex optimisation with a cost-volume to efficiently and effectively reconstruct a smooth model while maintaining depth discontinuity. The proposed approach has been validated by quan-titative evaluation using simulation and real phantom data with known ground truth. We also report qualitative results from real surgical im-ages. The algorithm outperforms state of the art methods and can be easily parallelised to run in real-time on recent graphics hardware.