KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers. In this paper, we take a holistic view on the registration problem and develop an open-source and versatile C++ library for point cloud registration, called \textit{KISS-Matcher}. KISS-Matcher combines a novel feature detector, \textit{Faster-PFH}, that improves over the classical fast point feature histogram (FPFH). Moreover, it adopts a $k$-core-based graph-theoretic pruning to reduce the time complexity of rejecting outlier correspondences. Finally, it combines these modules in a complete, user-friendly, and ready-to-use pipeline. As verified by extensive experiments, KISS-Matcher has superior scalability and broad applicability, achieving a substantial speed-up compared to state-of-the-art outlier-robust registration pipelines while preserving accuracy. Our code will be available at \href{https://github.com/MIT-SPARK/KISS-Matcher}{\texttt{https://github.com/MIT-SPARK/KISS-Matcher}}.
Code (1)
Tasks
Point Cloud RegistrationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
iMatcher: Improve matching in point cloud registration via local-to-global geometric consistency learning
This paper presents iMatcher, a fully differentiable framework for feature matching in point cloud registration. The proposed method leverages learned features to predict a geometrically consistent confidence matrix, inc…
Point Cloud RegistrationGraph EmbeddingPose EstimationGaussReg: Fast 3D Registration with Gaussian Splatting
Point cloud registration is a fundamental problem for large-scale 3D scene scanning and reconstruction. With the help of deep learning, registration methods have evolved significantly, reaching a nearly-mature stage. As …
NeRFPoint Cloud RegistrationIncremental Multiview Point Cloud Registration
In this paper, we present a novel approach for multiview point cloud registration. Different from previous researches that typically employ a global scheme for multiview registration, we propose to adopt an incremental p…
3D ReconstructionPoint Cloud RegistrationRetrievalImage-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling
Image-to-Point Cloud Registration (I2P) is essential for integrating camera and LiDAR in perception and autonomous systems, yet the modality gap between images and point clouds makes it difficult to achieve both high acc…
Point Cloud RegistrationPoint CloudsPROSE: Training-Free Egocentric Scene Registration with Vision-Language Models
Registering two captures of the same indoor space taken at different times underpins persistent spatial memory for robots and AR systems, yet the realistic version of this task is egocentric and its most scalable form is…
Scene UnderstandingPoint Clouds