3D Camera Pose Estimation via 2D Feature Mapping
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Solution Overview
Problem
Current methods for estimating the 3D camera pose in laparoscopic procedures are manual and impractical, as they require surgeons or assistants to manually select 2D feature points from 2D images and corresponding 3D points on a 3D model, which is slow, cumbersome, and difficult to perform continuously during surgeries.
Innovation Solution
A method and system for estimating 3D camera pose based on 2D image features, which involves generating 3D virtual camera poses, extracting 2D features from images, pairing these features with corresponding 3D poses, and creating a 2D feature-camera pose mapping model. This model is then used to map input 2D features from real-time images to an initial 3D pose estimate, which is refined through differential rendering of the 3D model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of 2D feature points and corresponding 3D points is used for camera pose estimation, then registration accuracy can be achieved, but the process becomes slow and impractical for real-time surgical guidance
Solution Approach 1:
The system pre-extracts 2D image features from surgical images and pre-establishes the mapping relationship between 2D features and 3D model points before surgery. This preliminary processing creates a ready-to-use feature database that enables rapid pose estimation during surgery without requiring manual point selection, thus resolving the contradiction between accuracy and speed.
Solution Approach 2:
The patent replaces the manual mechanical process of selecting feature points with an automated computer vision system. The system automatically extracts 2D features from images and matches them with corresponding 3D model points using algorithms, eliminating the need for manual intervention and enabling real-time processing while maintaining registration accuracy.
2Reliability
If manual feature point selection is performed continuously during surgery, then updated pose estimation can be obtained, but the complexity and difficulty of operation increase significantly
Solution Approach 1:
The system performs self-service by automatically extracting 2D features from surgical images and computing camera pose estimates without requiring surgeon or assistant intervention. The automated pipeline continuously processes images and updates pose information, making the system easy to operate while maintaining reliable and accurate pose estimation throughout the procedure.
3Loss of information
If 3D model registration with 2D images is performed manually, then visual guidance effectiveness can be enhanced, but the time required for setup and continuous updates increases
Solution Approach 1:
The system performs preliminary registration by pre-extracting 2D features from surgical images and pre-establishing the mapping relationship between these features and the 3D model before the surgical procedure begins. This advance preparation enables rapid pose estimation during surgery without requiring time-consuming manual point selection and registration, thus resolving the contradiction between visual information completeness and time efficiency.
Data Source
AI summary
Method and system for estimating 3D camera pose based on 2D features. 3D virtual camera poses are generated, each of which is used to determine a perspective to project a 3D model of a target organ to create a 2D image of the target organ. 2D features are extracted from each 2D image and paired with the corresponding 3D virtual camera pose to represent a mapping. A 2D feature-camera pose mapping model is obtained based on the pairs. Input 2D features extracted from a real-time 2D image of the target organ are used to map, via the 2D feature-camera pose mapping model, to a 3D pose estimate of a laparoscopic camera, which is then refined to derive an estimated 3D camera pose of the laparoscopic camera via differential rendering of the 3D model with respect to the 3D pose estimate.