Summed volume region selection based three-dimensional automatic target recognition for airborne LIDAR

Qian, Qishu; Hu, Yihua; Zhao, Naiqin; Li, Min-le; Shao, Fucai

doi:10.1016/j.dt.2019.10.011

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…* Author to whom any correspondence should be addressed. multidimensional information such as the geometry, color, and reflectivity of scanned objects, they have vital applications in numerous domains, including automatic object recognition [1,2], 3D surface reconstruction [3], structure detection [4], terrain mapping [5], and building information modeling [6] et al Current scanning technology has the qualities of high speed and high resolution and can generate dense point cloud data on the surface information of the scanned object owing to the development of 3D scanning equipment and 3D data acquisition technology. Data processing is challenging because of the enormous amount of point cloud data that has been obtained, and this includes several issues such as high computational cost, large memory footprints, and troublesome data transport.…”

Section: Introductionmentioning

confidence: 99%

Fast subsampling strategy for point cloud based on novel octree coding

Zheng,

Wang,

Zha

et al. 2024

Meas. Sci. Technol.

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Owing to the continuous expansion in data scale, the calculation, storage, and transmission of 3D data have been plagued by numerous issues. The point cloud data, in particular, often contain duplicated and anomalous points, which can hinder tasks such as measurement. To address this issue, it is crucial to utilize point cloud preprocessing methods that combine subsampling and denoising. These methods help obtain clean, evenly distributed, and compact points to enhance the accuracy of the data. In this study, an efficient point cloud subsampling method is proposed that combines point cloud denoising capabilities. This method can effectively preserve salient features while improving the quality of point cloud data. By constructing the octree structure of the point cloud, the corresponding node code is obtained according to the spatial coordinates of the point cloud, and the feature vector of the node is calculated based on the analysis of covariance. Node feature similarity is introduced to distinguish the node into feature and non-feature nodes, forming the node feature code, and the layer threshold is introduced to filter outliers. Experimental results showed that this approach is effective in removing noise while preserving important features, thereby reducing the complexity of the point cloud. The algorithm can serve as a reference for fast and precise measurement that demands high timeliness owing to its high computational efficiency.

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Section: Introductionmentioning

confidence: 99%

Fast subsampling strategy for point cloud based on novel octree coding

Zheng,

Wang,

Zha

et al. 2024

Meas. Sci. Technol.

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“…22,23 Currently, there are two main approaches to use point cloud data to identify and detect 3D objects: (1) pre-processing the point cloud data first, and then using the pre-processed data to detect and identify 3D objects. 24,25 This approach consists of two main solutions: first, the one is to divide the point cloud into voxels with spatial dependencies, and then use CNN for feature extraction with each voxel as a unit. Although, this approach can preserve the spatial location information of the point cloud, it causes a large computational resource consumption due to the amount of data and the huge amount of computation that the CNN itself requires.…”

Section: Introductionmentioning

confidence: 99%

MBBOS-GCN: minimum bounding box over-segmentation—graph convolution 3D point cloud deep learning model

Zhan

Dong-dong

Jin

et al. 2022

J. Appl. Rem. Sens.

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Point cloud data with high accuracy and high density is an important data source for the depiction of real ground objects, and there is a broad research prospect of using point cloud data directly for 3D object detection and recognition using deep learning methods. However, many deep learning models in previous research ignored the point cloud structure information and the sampling randomness. To overcome this limitation, we proposed an innovative 3D point cloud deep learning model, namely, the minimum bounding box over-segmentation-graph convolution 3D point cloud deep learning network model (MBBOS-GCN) for enhancing the structural information perception capability of the model and reduce the sampling randomness. In MBBOS-GCN, the number of points sampled is used as the scale, and a modified graph convolution model is used to collect point cloud structure information from different scales. The point cloud is divided into several small regions by the minimum bounding box algorithm, and the farthest point sampling (FPS) algorithm is used to sample within each small region to reduce sampling randomness. The experiments on object classification and semantic scene data segmentation show that: (1) the MBBOS-GCN model has high classification and segmentation accuracy, which is up to 91.87% and 89.5% on the ModelNet40 dataset and ScanNet dataset, respectively; (2) the MBBOS-GCN model is provided has good stability and robustness with a little change in accuracy under the altering density of input point cloud data, and slight classification loss value; (3) the MBBOS-GCN model can be adapted to real complex scenes when the classification accuracy reaches up to 97.53%. These superior performance of the MBBOS-GCN model can provide an effective support for the construction of digital twin city background data and the calibration of multimode satellite feature inversion algorithm validation. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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