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A 3D local feature description algorithm based on point distribution
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DOI：10.1016/j.compeleceng.2024.109341.png)
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It is well known that the local feature descriptor is playing an important role in 3D image recognition. But the current local feature descriptors have poor ability to describe target features in situations such as noise interference, be obscured, and changes in data resolution. On the basis of point distribution, we propose a new 3D local feature descriptor, the Point Distribution Signature Histogram(PDSH), which is relatively simple and easy to implement and has a fast identification speed by simplifying Local Reference Frame(LRF). We also rotate the local surface of the point cloud from multiple angles and project them onto three coordinate planes of the LRF. The projection planes are segmented at the same angle, and the distance information of the edge points and the count of projected points in each segment are statistically accumulated. Finally, the sub-feature vectors of the three coordinate planes are concatenated to obtain the feature descriptor of the key points. Experimental results show that our PDSH descriptor achieves a descriptiveness of 98 % on the pollution-free point cloud data set and better performance than the classical descriptors.
Keyword:
3D local feature descriptor
Local surface
Multi-angle rotation
Multi-view statistics
Point cloud registration
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4.9
论文数:
6.7K
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