With increasing demand on intra-operative navigation and motion compensation during robotic assisted minimally invasive surgery, real-time 3D deformation recovery remains a central problem. Currently the majority of existing methods rely on salient features, where the inherent paucity of distinctive landmarks implies either a semi-dense reconstruction or the use of strong geometrical constraints. In this study, we propose a gaze-contingent depth reconstruction scheme by integrating human perception with semi-dense stereo and p-q based shading information. Depth inference is carried out in real-time through a novel application of Bayesian chains without smoothness priors. The practical value of the scheme is highlighted by detailed validation using a beating heart phantom model with known geometry to verify the performance of gaze-contingent 3D surface reconstruction and deformation recovery. © 2009 Springer-Verlag.
CITATION STYLE
Visentini-Scarzanella, M., Mylonas, G. P., Stoyanov, D., & Yang, G. Z. (2009). i-BRUSH: A gaze-contingent virtual paintbrush for dense 3D reconstruction in robotic assisted surgery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5761 LNCS, pp. 353–360). https://doi.org/10.1007/978-3-642-04268-3_44
Mendeley helps you to discover research relevant for your work.