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A new point cloud simplification method for reducing visual distortion
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AI
DOI：10.1016/j.measurement.2024.114400.png)
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Abstract
En 中文
Existing point cloud simplification methods often focus on geometric fidelity while paying little attention to the visual distortion they cause. As a result, the simplified point cloud cannot meet the visualization requirements well. This study proposes a new simplification method that significantly reduces the visual distortion in the simplified point cloud. The proposed method begins by establishing a visual information loss function of the simplified point cloud and minimizing it, resulting in a set of simplified point clouds. Then, a quantification criterion for visual distortion in the simplified point cloud is established. By the criterion, point clouds are projected onto geometric and color feature domains that are closely related to visual effects, and the simplified point cloud with a small visual difference from the original point cloud is generated. The effectiveness of the proposed method has been verified using benchmark datasets and a scanned substation's massive point cloud.
Keywords:
Point cloud
Simplification
Visual information loss
Visual distortion
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