With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the co...
International audienceNormal segmentation of geometric range data has been a common practice integra...
This paper addresses the registration of LiDAR point clouds. More specifically, we present an automa...
The existing registration algorithms suffer from low precision and slow speed when registering a lar...
With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point cl...
Registration is usually the first step for the usage of point cloud data. Registration of point clou...
International audienceData acquisition in large-scale scenes regularly involves accumulating informa...
input model scan Q scan P SUPER 4PCS (without ICP) GLS-based matching points Figure 1: We present SU...
input model scan Q scan P SUPER 4PCS (without ICP) GLS-based matching points Figure 1: We present SU...
input model scan Q scan P SUPER 4PCS (without ICP) GLS-based matching points Figure 1: We present SU...
Entire surface point clouds in complex objects cannot be captured in a single direction by using non...
In this paper, we propose a method for registering unorganized point clouds without using targets or...
Scans acquired by 3D sensors are typically represented in a local coordinate system. When multiple s...
To address the registration problem in current machine vision, a new three-dimensional (3-D) point c...
To address the registration problem in current machine vision, a new three-dimensional (3-D) point c...
International audienceNormal segmentation of geometric range data has been a common practice integra...
International audienceNormal segmentation of geometric range data has been a common practice integra...
This paper addresses the registration of LiDAR point clouds. More specifically, we present an automa...
The existing registration algorithms suffer from low precision and slow speed when registering a lar...
With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point cl...
Registration is usually the first step for the usage of point cloud data. Registration of point clou...
International audienceData acquisition in large-scale scenes regularly involves accumulating informa...
input model scan Q scan P SUPER 4PCS (without ICP) GLS-based matching points Figure 1: We present SU...
input model scan Q scan P SUPER 4PCS (without ICP) GLS-based matching points Figure 1: We present SU...
input model scan Q scan P SUPER 4PCS (without ICP) GLS-based matching points Figure 1: We present SU...
Entire surface point clouds in complex objects cannot be captured in a single direction by using non...
In this paper, we propose a method for registering unorganized point clouds without using targets or...
Scans acquired by 3D sensors are typically represented in a local coordinate system. When multiple s...
To address the registration problem in current machine vision, a new three-dimensional (3-D) point c...
To address the registration problem in current machine vision, a new three-dimensional (3-D) point c...
International audienceNormal segmentation of geometric range data has been a common practice integra...
International audienceNormal segmentation of geometric range data has been a common practice integra...
This paper addresses the registration of LiDAR point clouds. More specifically, we present an automa...
The existing registration algorithms suffer from low precision and slow speed when registering a lar...