NICP: Dense Normal Based Point Cloud Registration
In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least squares formulation of the alignment problem, that minimizes an error metric depending on these surface characteristics. We named the approach Normal Iterative Closest Point (NICP in short). Extensive experiments on publicly available benchmark data show that NICP outperforms other state-of-the-art approaches.
Code (1)
Tasks
Point Cloud RegistrationSimilar Papers 제목 키워드 기반
A dynamic memory assignment strategy for dilation-based ICP algorithm on embedded GPUs
This paper proposes a memory-efficient optimization strategy for the high-performance point cloud registration algorithm VANICP, enabling lightweight execution on embedded GPUs with constrained hardware resources. VANICP…
Point Cloud RegistrationComputational EfficiencyNICP: Neural ICP for 3D Human Registration at Scale
Aligning a template to 3D human point clouds is a long-standing problem crucial for tasks like animation, reconstruction, and enabling supervised learning pipelines. Recent data-driven methods leverage predicted surface …
GLASS: Geometry-aware Local Alignment and Structure Synchronization Network for 2D-3D Registration
Image-to-point cloud registration methods typically follow a coarse-to-fine pipeline, extracting patch-level correspondences and refining them into dense pixel-to-point matches. However, in scenes with repetitive pattern…
Point Cloud RegistrationPoint CloudsRDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving
Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud regi…
Autonomous DrivingPoint Cloud RegistrationPose EstimationTacLoc: Global Tactile Localization on Objects from a Registration Perspective
Pose estimation is essential for robotic manipulation, particularly when visual perception is occluded during gripper-object interactions. Existing tactile-based methods generally rely on tactile simulation or pre-traine…
Point Cloud RegistrationPose EstimationPoint Clouds