Grouped 3D Point Sampling for RANSAC Efficiency
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Solution Overview
Problem
Existing methods for computing models from data points, especially in noisy environments, face high computational costs when the inlier ratio is low, leading to inefficient processing and potential failure to achieve a solution within a desired time frame.
Innovation Solution
A modified consensus estimator approach that groups reconstructed 3D points based on attributes and selects high-quality groups for sampling, biasing the sampling process to input these groups first to the core consensus estimator, such as RANSAC, to rapidly converge on a correct solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional consensus estimators sample minimal points from data to compute models, then the method can handle noisy data and compute models, but the computational cost becomes too high when the inlier ratio is low
Solution Approach 1:
The patent applies preliminary action by performing data grouping and quality assessment before the actual model computation. The system groups data points based on attribute similarity and pre-identifies high-quality groups, so that when the consensus estimator runs, it can focus computational resources on the most promising data subsets, reducing overall computational cost while maintaining reliability
Solution Approach 2:
The patent implements local quality by treating different data groups differently based on their quality characteristics. High-quality groups (with higher inlier ratios) are prioritized for sampling and model computation, while low-quality groups are either skipped or processed with reduced computational intensity, optimizing the balance between model accuracy and computational efficiency
2Reliability
If traditional methods repeatedly sample and compute best fit models, then a solution can be achieved, but the processing time exceeds the desired time frame when inlier ratio is low
Solution Approach 1:
The system performs preliminary grouping and quality assessment of data before initiating the iterative model computation. By pre-identifying high-quality data groups with higher inlier ratios, the system can reduce the number of iterative sampling attempts needed, thereby decreasing processing time while maintaining solution accuracy
Solution Approach 2:
The patent changes the sampling parameters dynamically based on data group quality. Instead of uniform random sampling, the system adjusts sampling probabilities and selection criteria based on the inlier ratio and quality metrics of different data groups, enabling faster convergence to accurate solutions by prioritizing promising data subsets
Data Source
AI summary
In one embodiment, a computer accessible storage medium stores a plurality of instructions which, when executed: group a set of reconstructed three dimensional (3D) points derived from image data into a plurality of groups based on one or more attributes of the 3D points; select one or more groups from the plurality of groups; and sample data from the selected groups, wherein the sampled data is input to a consensus estimator to generate a model that describes a 3D model of a scene captured by the image data. Other embodiments may bias sampling into a consensus estimator for any data set, based on relative quality of the data set.