Gaussian Curvature Criterion based Random Sample Matching for Improved 3D Registration

We propose a novel Gaussian Curvature (GC) based criterion to discard false point correspondences within the RANdom SAmple Matching (RANSAM) framework to improve the 3D registration. The RANSAM method is used to find a point pair correspondence match between two surfaces and GC is used to verify whether this point pair is a correct (similar curvatures) or false (dissimilar curvatures) match. The point pairs which pass the curvature test are used to compute a transformation which aligns the two overlapping surfaces. The results on shape alignment benchmarks show improved accuracy of the GRANSAM versus RANSAM and six other registration methods while maintaining efficiency.