This paper proposes a novel local feature descriptor, called a local feature statistics his-togra... more This paper proposes a novel local feature descriptor, called a local feature statistics his-togram (LFSH), for efficient 3D point cloud registration. An LFSH forms a comprehensive description of local shape geometries by encoding their statistical properties on local depth, point density, and angles between normals. The sub-features in the LFSH descriptor are low-dimensional and quite efficient to compute. In addition, an optimized sample consensus (OSAC) algorithm is developed to iteratively estimate the optimum transformation from point correspondences. OSAC can handle the challenging cases of matching highly self-similar models. Based on the proposed LFSH and OSAC, a coarse-to-fine algorithm can be formed for 3D point cloud registration. Experiments and comparisons with the state-of-the-art descriptors demonstrate that LFSH is highly discriminative, robust, and significantly faster than other descriptors. Meanwhile, the proposed coarse-to-fine registration algorithm is demonstrated to be robust to common nuisances, including noise and varying point cloud resolutions, and can achieve high accuracy on both model data and scene data.
Point cloud source data for surface reconstruction is usually contaminated with noise and outlier... more Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean-shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.
In this chapter we will deal with three dimensional (3D) point cloud data. They are a subset of p... more In this chapter we will deal with three dimensional (3D) point cloud data. They are a subset of point data, where each point contains an attribute with height information. As for all point data, data can, but must not be stored in a regular grid. Vector point data come in various formats, including those discussed in Chap. 2 and spatially enabled relational database management systems (PostGIS, Spatialite). We concentrate on a specific type of 3D point data, obtained with laser scanning instruments. The example data are obtained from an airborne topographic instrument. A thorough discussion on point cloud data is beyond the scope of this book. The open source community for point cloud data is not as well established and consolidated as it is for vector and raster data. Standards are still under development, in particular with respect to the relatively new full waveform (FWF) data. We provide a brief introduction on LiDAR data and how to process them. This ranges from extracting basic information to filtering techniques to derive different digital elevation models (DEM). A selection of current open source utilities is also presented here, without entering into as much detail as for vector and raster data. In addition, the toolbox offered by SPDLib is discussed more in depth in the case study in Chap. 18.
This paper proposes a novel local feature descriptor, called a local feature statistics his-togra... more This paper proposes a novel local feature descriptor, called a local feature statistics his-togram (LFSH), for efficient 3D point cloud registration. An LFSH forms a comprehensive description of local shape geometries by encoding their statistical properties on local depth, point density, and angles between normals. The sub-features in the LFSH descriptor are low-dimensional and quite efficient to compute. In addition, an optimized sample consensus (OSAC) algorithm is developed to iteratively estimate the optimum transformation from point correspondences. OSAC can handle the challenging cases of matching highly self-similar models. Based on the proposed LFSH and OSAC, a coarse-to-fine algorithm can be formed for 3D point cloud registration. Experiments and comparisons with the state-of-the-art descriptors demonstrate that LFSH is highly discriminative, robust, and significantly faster than other descriptors. Meanwhile, the proposed coarse-to-fine registration algorithm is demonstrated to be robust to common nuisances, including noise and varying point cloud resolutions, and can achieve high accuracy on both model data and scene data.
Point cloud source data for surface reconstruction is usually contaminated with noise and outlier... more Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean-shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.
In this chapter we will deal with three dimensional (3D) point cloud data. They are a subset of p... more In this chapter we will deal with three dimensional (3D) point cloud data. They are a subset of point data, where each point contains an attribute with height information. As for all point data, data can, but must not be stored in a regular grid. Vector point data come in various formats, including those discussed in Chap. 2 and spatially enabled relational database management systems (PostGIS, Spatialite). We concentrate on a specific type of 3D point data, obtained with laser scanning instruments. The example data are obtained from an airborne topographic instrument. A thorough discussion on point cloud data is beyond the scope of this book. The open source community for point cloud data is not as well established and consolidated as it is for vector and raster data. Standards are still under development, in particular with respect to the relatively new full waveform (FWF) data. We provide a brief introduction on LiDAR data and how to process them. This ranges from extracting basic information to filtering techniques to derive different digital elevation models (DEM). A selection of current open source utilities is also presented here, without entering into as much detail as for vector and raster data. In addition, the toolbox offered by SPDLib is discussed more in depth in the case study in Chap. 18.
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