Title
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 minimally 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 textureless tissue appearance in the real surgical scene and occlusion by instruments. To tackle these problems, we propose a dense stereo reconstruction 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 quantitative evaluation using simulation and real phantom data with known ground truth. We also report qualitative results from real surgical images. The algorithm outperforms state of the art methods and can be easily parallelised to run in real-time on recent graphics hardware.
Year
DOI
Venue
2013
10.1007/978-3-642-40811-3_6
Lecture Notes in Computer Science
Keywords
Field
DocType
three dimensional,robotics,imaging,surgery,algorithms
Computer vision,Graphics hardware,Computer science,Imaging phantom,Discontinuity (linguistics),Robotic surgery,Regular polygon,Ground truth,Stereo reconstruction,Artificial intelligence
Conference
Volume
Issue
ISSN
8149
Pt 1
0302-9743
Citations 
PageRank 
References 
8
0.56
10
Authors
4
Name
Order
Citations
PageRank
Ping-Lin Chang1698.02
Danail Stoyanov279281.36
Andrew J. Davison36707350.85
Philip Eddie Edwards480.56