Dynamic Auxiliary Camera Calibration for Accurate 3D Scan Colorization
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Solution Overview
Problem
Existing 3D scanners face challenges in efficiently capturing high-quality color images due to the time-consuming nature of high dynamic range imaging techniques and the need for precise calibration of auxiliary cameras with ultrawide-angle lenses, which often result in systematic errors and inaccurate colorization of 3D point clouds.
Innovation Solution
A system and method for dynamically calibrating an auxiliary camera using a 3D point cloud, control image, and calibration image, involving feature extraction, matching, and self-calibration to establish control points, which includes transforming ultrawide-angle images to spherical images for improved feature matching and using bundle adjustment with image clusters for accurate calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high dynamic range imaging techniques are used to capture color images, then the color accuracy of the 3D scan is improved, but the time required to capture images increases significantly
Solution Approach 1:
The system performs preliminary calibration of the auxiliary camera using the 3D point cloud and control image before capturing the final color images. This pre-calibration establishes accurate mapping relationships and camera parameters, enabling subsequent HDR image capture to proceed more efficiently with proper exposure settings predetermined, thus reducing the overall time required while maintaining color accuracy
Solution Approach 2:
The patent introduces a control image as an intermediary element that facilitates the calibration process. The control image serves as a reference that bridges the 3D point cloud data and the auxiliary camera images, enabling accurate feature matching and calibration without requiring multiple lengthy HDR exposures. This intermediary approach streamlines the color accuracy achievement process
2Measurement precision
If auxiliary camera calibration is performed using traditional methods, then the mapping accuracy between 2D images and 3D coordinates is improved, but the calibration process becomes complex and time-consuming
Solution Approach 1:
The system performs self-calibration by automatically extracting features from the control image and auxiliary camera images, matching them, and computing camera parameters without requiring manual intervention or complex external calibration equipment. The 3D point cloud itself serves as the calibration reference, eliminating the need for separate calibration targets or procedures, thus reducing complexity while maintaining high mapping accuracy
Solution Approach 2:
The patent merges the calibration process with the normal scanning operation by using the control image captured during scanning as the calibration reference. Instead of separating calibration into a distinct complex procedure, the system combines both functions, using the same imaging data for both calibration and final rendering, thereby simplifying the overall process while achieving accurate mapping
3Measurement precision
If multiple images with different exposures are captured for HDR imaging, then the color representation accuracy is improved, but the productivity of the scanning process deteriorates
Solution Approach 1:
The system determines optimal exposure settings in advance during the calibration phase using the control image and 3D point cloud. By pre-calculating the appropriate exposure parameters based on the scene characteristics and camera properties, the system minimizes the number of exposures needed during actual scanning while still achieving accurate color representation, thus improving productivity without sacrificing color accuracy
Solution Approach 2:
The patent dynamically adjusts imaging parameters including exposure time, gain, and other camera settings based on the calibration results and real-time scanning conditions. By optimizing these parameters beforehand and adapting them during scanning, the system achieves high-quality color images with fewer shots required, thereby maintaining productivity while ensuring accurate color representation
Data Source
AI summary
A method includes capturing, by a three-dimensional (3D) scanner, a 3D point cloud, and capturing, by a camera, a control image by capturing and stitching multiple images of the surrounding environment. The method further includes capturing, by an auxiliary camera, an ultrawide-angle calibration image. The method further includes dynamically calibrating the auxiliary camera using the 3D point cloud, the control image, and the calibration image. The calibrating includes extracting a first plurality of features from the control image and extracting a second plurality of features from the calibration image. Further, a set of matching features are determined from the first and second sets of features. A set of control points is generated using the set of matching features by determining points in the 3D point cloud that correspond to the set of matching features. Further, a self-calibration of the auxiliary camera is performed using the set of control points.