Abstract
Abstract. Point clouds acquired by RGB-D camera-based indoor mobile mapping system suffer the problems of being noisy, exhibiting an uneven distribution, and incompleteness, which are the problems that introduce difficulties for point cloud planar surface segmentation. This paper presents a novel color-enhanced hybrid planar surface segmentation model for RGB-D camera-based indoor mobile mapping point clouds based on region growing method, and the model specially addresses the planar surface extraction task over point cloud according to the noisy and incomplete indoor mobile mapping point clouds. The proposed model combines the color moments features with the curvature feature to select the seed points better. Additionally, a more robust growing criteria based on the hybrid features is developed to avoid the generation of excessive over-segmentation debris. A segmentation evaluation process with a small set of labeled segmented data is used to determine the optimal hybrid weight. Several comparative experiments were conducted to evaluate the segmentation model, and the experimental results demonstrate the effectiveness and efficiency of the proposed hybrid segmentation method for indoor mobile mapping three-dimensional (3D) point cloud data.
Highlights
With the huge demands of emergency response simulation and training, cultural heritage protection, digital city, and other related applications, the indoor mobile mapping system (Pathak et al, 2009, Bouvrie et al, 2011), which integrated the mobile platform with positioning sensors, laser scanners, optical cameras and other sensors, provides a highly efficient way to obtain threedimensional (3D) point cloud data for an indoor environment
The point cloud data acquired by the RGB-D camera-based indoor mobile mapping system suffers the problems of being noisy, exhibiting an uneven distribution, low resolution and incompleteness (Han et al, 2013)
A color-enhanced hybrid segmentation model based on the region growing method is proposed for RGB-D camerabased indoor mobile mapping point cloud planar surface segmentation, and the model is more robust to the clustered, noisy and incomplete point cloud data compared to the traditional point cloud segmentation method
Summary
With the huge demands of emergency response simulation and training, cultural heritage protection, digital city, and other related applications, the indoor mobile mapping system (Pathak et al, 2009, Bouvrie et al, 2011), which integrated the mobile platform with positioning sensors, laser scanners, optical cameras and other sensors, provides a highly efficient way to obtain threedimensional (3D) point cloud data for an indoor environment. The point cloud data acquired by the RGB-D camera-based indoor mobile mapping system suffers the problems of being noisy, exhibiting an uneven distribution, low resolution and incompleteness (Han et al, 2013). These data quality problems are much more obvious than the point cloud data acquired by a laser scanner. A color-enhanced hybrid segmentation model based on the region growing method is proposed for RGB-D camerabased indoor mobile mapping point cloud planar surface segmentation, and the model is more robust to the clustered, noisy and incomplete point cloud data compared to the traditional point cloud segmentation method. The segmentation results are given based on the hybrid weight effect on the segmentation performance comparison between the segmentation methods
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