3 accepted papers
We introduce C-GenReg, a training-free framework for 3D point cloud registration that leverages the complementary strengths of world-scale generative priors and registration-oriented Vision Foundation Models (VFMs). Current learning-based 3D point cloud registration methods struggle to generalize ac
"In this paper, we adopt the Universal Manifold Embedding (UME) framework for the estimation of rigid transformations and extend it, so that it can accommodate scenarios involving partial overlap and differently sampled point clouds. UME is a methodology designed for mapping observations of the same…
We present a closed form solution to the problem of registration and detection of dense 3-D point clouds undergoing unknown rigid deformations. The solution is obtained by adapting the general framework of the universal manifold embedding (UME) to the case where the deformations the object may under…