5 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
Radar-based object detection is essential for autonomous driving due to radar's long detection range. However, the sparsity of radar point clouds, especially at long range, poses challenges for accurate detection. Existing methods increase point density through temporal aggregation with ego-motion c…
We consider the problems of estimating the underlying transformation and the detection of 3-D objects undergoing rigid transformations. It has been shown that the Rigid Transformation Universal Manifold Embedding (RTUME) provides a mapping from the set of all possible observations on some object to…
Object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However obtaining annotated datasets from real radar images crucial for training these networks is challenging especially in scenarios with long-range detection and adverse weather and ligh…
We consider the problem of classifying 3-D objects undergoing rigid transformations. It has been shown that the rigid transformation universal manifold embedding (RTUME) provides a mapping from the orbit of observations on some object to a single low-dimensional linear subspace of Euclidean space. T…