IVCVDec 31, 2024

Tech Report: Divide and Conquer 3D Real-Time Reconstruction for Improved IGS

arXiv:2501.01465v1
Originality Synthesis-oriented
AI Analysis

This work addresses the problem of intraoperative surgical guidance for clinicians, but appears to be an incremental improvement through integration of existing methods.

The authors tackled the challenge of real-time 3D reconstruction from endoscopic videos for surgical tracking by developing a modular pipeline that integrates frame selection, depth estimation, and 3D reconstruction components. Experiments on the Hamlyn dataset demonstrated the effectiveness of their integrated methods, though no concrete numerical results were provided.

Tracking surgical modifications based on endoscopic videos is technically feasible and of great clinical advantages; however, it still remains challenging. This report presents a modular pipeline to divide and conquer the clinical challenges in the process. The pipeline integrates frame selection, depth estimation, and 3D reconstruction components, allowing for flexibility and adaptability in incorporating new methods. Recent advancements, including the integration of Depth-Anything V2 and EndoDAC for depth estimation, as well as improvements in the Iterative Closest Point (ICP) alignment process, are detailed. Experiments conducted on the Hamlyn dataset demonstrate the effectiveness of the integrated methods. System capability and limitations are both discussed.

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