Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Robust Dense Matching Using Local and Global Geometric Constraints
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Towards Endoscopic Augmented Reality for Robotically Assisted Minimally Invasive Cardiac Surgery
MIAR '01 Proceedings of the International Workshop on Medical Imaging and Augmented Reality (MIAR '01)
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Motion compensated SLAM for image guided surgery
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Real-time stereo reconstruction in robotically assisted minimally invasive surgery
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Stereoscopic Scene Flow Computation for 3D Motion Understanding
International Journal of Computer Vision
Scene flow estimation by growing correspondence seeds
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Information about the 3D shape and motion of tissue surfaces at the surgical site during minimally invasive surgery is important for providing metric measurements that enable the deployment of image-guidance and enhanced robotic control. This article presents a scene flow algorithm that recovers the deformation and 3D structure of the surgical field-of-view from stereoscopic images by propagating information starting from a sparse set of candidate seed matches. By imposing spatial and temporal constraints the proposed algorithm is able to reconstruct dense 3D scene flow accurately and efficiently. Validation is performed using simulation data to evaluate the method against varying levels of image noise and results are also presented for benchmark phantom model data. The practical value of proposed method is shown by qualitative results for in vivo videos from robotic assisted procedures.