Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
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TLDR
A new method for point cloud simplification, named FPPS (feature-preserved point clouds simplification), by which the simplification rules are set and the results show that FPPS is superior to other simplification algorithms.Abstract:
With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.read more
Citations
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Point cloud simplification algorithm based on the feature of adaptive curvature entropy
TL;DR: Compared to traditional curvature simplification algorithms, the proposed method has the lowest deviation and highest accuracy at the same simplicity level, as numerous feature points are preserved, which facilitates the point cloud processing.
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Geometry Preserving Sampling Method Based on Spectral Decomposition for Large-Scale Environments.
TL;DR: A novel sampling algorithm based on spectral decomposition analysis to derive local density measures for each geometric primitive, which identifies and preserves geometric information along the topology of point clouds and is able to scale to large environments with a non-uniform density.
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TL;DR: Wang et al. as discussed by the authors developed an adaptive down-sampling method, which is able to maintain scan points containing critical geometric or semantic information and only down-sample scan points with no critical information.
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A New Simplification Algorithm for Scattered Point Clouds with Feature Preservation
TL;DR: Wang et al. as discussed by the authors proposed a new simplification algorithm for scattered point clouds with feature preservation, which can reduce the amount of data while retaining the features of data, and the method can be applied to models with different curvatures and effectively avoid the hole phenomenon in the simplification process.
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A Saliency-Based Sparse Representation Method for Point Cloud Simplification.
TL;DR: In this paper, a global approach to detect saliencies on a given point cloud is proposed, which estimates a feature vector for each point in the cloud and combines these features with the reconstruction error of each signal to produce the final saliency value.
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