In search of inliers: 3d correspondence by local and global voting

TL;DR

This paper introduces a fast voting-based method combining local geometric invariants and global covariant constraints to accurately identify inlier correspondences in 3D models, outperforming existing techniques.

Contribution

It proposes a novel 3D correspondence method that integrates local and global voting constraints with a guided sampling process for improved accuracy.

Findings

Superior performance over state-of-the-art methods.

Effective in controlled and comparative tests.

Potential application in 3D object detection.

Abstract

We present a method for finding correspondence between 3D models. From an initial set of feature correspondences, our method uses a fast voting scheme to separate the inliers from the outliers. The novelty of our method lies in the use of a combination of local and global constraints to determine if a vote should be cast. On a local scale, we use simple, low-level geometric invariants. On a global scale, we apply covariant constraints for finding compatible correspondences. We guide the sampling for collecting voters by downward dependencies on previous voting stages. All of this together results in an accurate matching procedure. We evaluate our algorithm by controlled and comparative testing on different datasets, giving superior performance compared to state of the art methods. In a final experiment, we apply our method for 3D object detection, showing potential use of our method within higher-level vision.