Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
Learning universal representations across different applications domain is an open research problem. In fact, finding universal architecture within the same application but across different types of datasets is still unsolved problem too, especially in applications involving processing 3D point clouds. In this work we experimentally test several state-of-the-art learning-based methods for 3D point cloud registration against the proposed non-learning baseline registration method. The proposed method either outperforms or achieves comparable results w.r.t. learning based methods. In addition, we propose a dataset on which learning based methods have a hard time to generalize. Our proposed method and dataset, along with the provided experiments, can be used in further research in studying effective solutions for universal representations. Our source code is available at: github.com/DavidBoja/greedy-grid-search.
Code (2)
Tasks
Point Cloud RegistrationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SDFReg: Learning Signed Distance Functions for Point Cloud Registration
Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy, partial, and density-varying point clouds. To this end, we propose a novel point c…
Point Cloud RegistrationVRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filtering
Addressing the challenges posed by the substantial gap in point cloud data collected from diverse sensors, achieving robust cross-source point cloud registration becomes a formidable task. In response, we present a novel…
Point Cloud RegistrationPointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics. However, registration tasks under challenging conditions, under which noise or p…
Autonomous DrivingPoint Cloud RegistrationMahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations
In this paper, we discuss Mahalanobis k-NN: A Statistical Lens designed to address the challenges of feature matching in learning-based point cloud registration when confronted with an arbitrary density of point clouds. …
BenchmarkingPoint Cloud RegistrationUMERegRobust -- Universal Manifold Embedding Compatible Features for Robust Point Cloud Registration
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 p…
Point Cloud Registration