An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-rigid Reconstruction of the Beating Heart Surface for Minimally Invasive Cardiac Surgery
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Toward Video-Based Navigation for Endoscopic Endonasal Skull Base Surgery
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Affine-invariant anisotropic detector for soft tissue tracking in minimally invasive surgery
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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
Robust 3d visual tracking for robotic-assisted cardiac interventions
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
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
Content-based surgical workflow representation using probabilistic motion modeling
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
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Accurate 3D reconstruction of the surgical scene is important in intra-operative guidance. Existing methods are often based on the assumption that the camera is static or the tissue is deforming with periodic motion. In minimally invasive surgery, these assumptions do not always hold due to free-form tissue deformation induced by instrument-tissue interaction and camera motion required for continuous exploration of the surgical scene, particularly for intraluminal procedures. The aim of this work is to propose a novel framework for intra-operative free-form deformation recovery. The proposed method builds on a compact scene representation scheme that is suitable for both surgical episode identification and instrument-tissue motion modeling. Unlike previous approaches, it does not impose explicit models on tissue deformation, allowing realistic free-form deformation recovery. Validation is provided on both synthetic and phantom data. The practical value of the method is further demonstrated by deformation recovery on in vivo data recorded from a robotic assisted minimally invasive surgical procedure.