Autocalibration for Sparse 3D Point Cloud Reconstruction
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Solution Overview
Problem
Current Structure from Motion (SfM) pipelines face challenges in estimating both internal and external camera parameters without ancillary information, and existing Multi-view Stereo (MVS) systems lack scalability and accuracy, especially in uncalibrated environments and large-scale outdoor reconstructions.
Innovation Solution
A method that uses a 2-step algorithm for autocalibration to extract internal camera parameters, followed by Euclidean reconstruction, and incorporates a hierarchical clustering approach to merge image clusters, along with a global outlier removal procedure and guided stereo matching using sparse 3D points and visibility information, to create a dense 3D model from sparse images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If classical SfM pipelines process images in batch without ancillary information, then the system can operate autonomously without external calibration data, but the accuracy and reliability of camera parameter estimation deteriorates
Solution Approach 1:
The patent introduces a hierarchical clustering procedure as an intermediary mechanism that groups images with similar internal parameters before processing. This clustering step acts as a mediator that organizes the uncalibrated images into manageable groups, enabling more accurate parameter estimation within each cluster while maintaining overall automation. The clustering-based approach bridges the gap between fully autonomous operation and high precision by structuring the data processing pipeline.
Solution Approach 2:
The patent segments the image processing pipeline into multiple hierarchical levels, where images are first clustered into groups based on similarity metrics, then processed separately within each cluster. This segmentation allows the system to handle uncalibrated images in a structured manner, improving estimation accuracy by reducing the complexity of the overall problem into smaller, more manageable sub-problems that can be solved with higher precision.
2Adaptability or versatility
If MVS systems are designed for large-scale outdoor reconstructions, then the system covers larger areas and more diverse scenes, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the large-scale reconstruction problem into smaller cluster-based sub-problems. Images are grouped into clusters based on their similarity and spatial relationships, allowing the MVS processing to be performed on smaller, more manageable subsets of data. This hierarchical clustering approach reduces the overall computational complexity while maintaining the ability to handle large-scale outdoor scenes by processing them in organized groups.
Solution Approach 2:
The patent introduces dynamic adaptability through its hierarchical clustering framework, which can adjust the level of processing detail and computational resources allocated based on the specific characteristics of each image cluster. The system dynamically organizes processing tasks according to the complexity and size of different scene regions, enabling efficient scaling from small to large-scale reconstructions without a linear increase in computational burden.
3Productivity
If autocalibration is performed without clustering procedures, then the processing pipeline is simpler and faster, but the reliability of camera parameter estimation deteriorates due to mixing different internal parameters
Solution Approach 1:
The patent segments the autocalibration process by introducing a clustering step that groups images with similar internal parameters before estimation. This segmentation maintains processing efficiency by organizing data into coherent groups that can be processed relatively quickly, while simultaneously improving reliability by ensuring that parameter estimation is performed on homogeneous subsets of data rather than mixing vastly different camera configurations.
Solution Approach 2:
The patent applies preliminary action by performing clustering before the actual autocalibration parameter estimation. This preliminary organization of images into clusters based on similarity metrics prepares the data in advance, so that when autocalibration is performed on each cluster, the internal parameters are more consistent and reliable. This pre-processing step ensures better estimation reliability without significantly impacting the overall processing timeline.
4Quantity of substance
If dense point cloud extraction is performed without sparse structure guidance, then the system can capture all details, but the accuracy and consistency of 3D reconstruction deteriorates
Solution Approach 1:
The patent applies preliminary action by first extracting sparse 3D structure information from the image set before performing dense point cloud extraction. This preliminary sparse structure serves as a guide and framework for the subsequent dense extraction process, ensuring that the dense points are distributed and positioned accurately according to the established 3D geometry. This two-stage approach maintains high measurement precision while achieving comprehensive point coverage.
Solution Approach 2:
The patent introduces the sparse 3D structure as an intermediary element that mediates between the 2D image data and the final dense 3D point cloud. This intermediate sparse structure provides geometric constraints and guidance that ensure the dense point extraction process produces accurate and consistent 3D reconstructions, bridging the gap between raw image data and high-precision 3D models.
Data Source
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AI summary
A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parameters.