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Hop-Diffusion Monte Carlo for Epipolar Geometry Estimation between Very Wide-Baseline Images.

Aveek S Brahmachari, Sudeep Sarkar

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 10, 2015

Summary

We developed a Monte Carlo method called BLOGS for robust epipolar geometry estimation. It efficiently finds accurate correspondences even with 90% outliers, outperforming other methods in speed and iteration count.

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Area of Science:

Computer Vision
Geometric Computer Vision
Robotics

Background:

Estimating epipolar geometry is crucial for 3D reconstruction and visual SLAM.
Wide baselines, scale changes, and occlusions introduce significant outliers in correspondence matching.
Existing methods struggle with high outlier rates, impacting accuracy and efficiency.

Purpose of the Study:

To introduce a novel Monte Carlo approach for robust epipolar geometry estimation.
To efficiently identify minimal sets of inlier correspondences amidst a high percentage of outliers.
To improve the speed and accuracy of epipolar geometry estimation in challenging scenarios.

Main Methods:

Balanced Local and Global Search (BLOGS) algorithm.
Local search employs a diffusion process with Joint Feature Distributions.
Global search utilizes a hopping process guided by photometric properties.

Main Results:

BLOGS demonstrates superior performance compared to MAPSAC, NAPSAC, and BEEM.
Achieves comparable results to existing methods but with 10x fewer iterations.
Exhibits the lowest time per iteration among studied approaches.

Conclusions:

BLOGS offers a highly efficient and robust solution for epipolar geometry estimation.
The method excels in scenarios with substantial outliers, common in real-world applications.
BLOGS significantly advances the state-of-the-art in robust geometric correspondence.