In search of inliers: 3d correspondence by local and global voting
This addresses the problem of 3D model correspondence for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of finding correspondence between 3D models by developing a voting scheme that separates inliers from outliers using local and global constraints, resulting in superior performance compared to state-of-the-art methods in controlled and comparative testing on different datasets.
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.