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Removing Stray Noise Quickly from Point Cloud Data Based on Sheep Model

Abstract : The noise data could be produced when we scanned the object by Handy 3D scanner due to human factors, the target surface and the instrument itself factors etc. Noised point cloud data could seriously affect the precision and efficiency of three-dimensional reconstruction in late stage. To this problem, we used the sheep body’s three-dimensional point cloud data and changed the algorithm of k-nearest neighbors and presented method that combined the k-nearest neighbor denoising and median filtering. Firstly, the improved k-nearest neighbors algorithm could establish topology relationship fast, identify and delete some noise data; then, using the filter method processed the point cloud data and all noise data could be identified and deleted. The experimental results show that the method we presented can eliminate the stray noise from the point cloud data quickly and accurately and keep ideal target.
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https://hal.inria.fr/hal-02179987
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Submitted on : Friday, July 12, 2019 - 11:20:29 AM
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Heru Xue, Xinhua Jiang, Yanqing Zhou, Liyan Wang, Chunlan Wang. Removing Stray Noise Quickly from Point Cloud Data Based on Sheep Model. 10th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2016, Dongying, China. pp.435-441, ⟨10.1007/978-3-030-06155-5_44⟩. ⟨hal-02179987⟩

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