References & Citations
Computer Science > Computer Vision and Pattern Recognition
Title: SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data
(Submitted on 25 Sep 2023 (v1), last revised 27 Mar 2024 (this version, v2))
Abstract: Manipulation of tissue with surgical tools often results in large deformations that current methods in tracking and reconstructing algorithms have not effectively addressed. A major source of tracking errors during large deformations stems from wrong data association between observed sensor measurements with previously tracked scene. To mitigate this issue, we present a surgical perception framework, SuPerPM, that leverages learning-based non-rigid point cloud matching for data association, thus accommodating larger deformations. The learning models typically require training data with ground truth point cloud correspondences, which is challenging or even impractical to collect in surgical environments. Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth. This was achieved by employing a position-based dynamics (PBD) simulation to ensure that the correspondences adhered to physical constraints. The proposed framework is demonstrated on several challenging surgical datasets that are characterized by large deformations, achieving superior performance over state-of-the-art surgical scene tracking algorithms.
Submission history
From: Shan Lin [view email][v1] Mon, 25 Sep 2023 04:27:06 GMT (7874kb,D)
[v2] Wed, 27 Mar 2024 21:15:27 GMT (7874kb,D)
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