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首页> 外文会议>Congreso Argentino de Ciencias de la Informatica y Desarrollos de Investigacion >Point Cloud Denoising using Deep Learning
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Point Cloud Denoising using Deep Learning

机译：点云去噪使用深度学习

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摘要

The emergence of uncontrollable noise from diverse sources possess several difficulties in scanning 3D objects. In the case of animals in the wild this is especially hard to manage since their movements are unavoidable during the acquisition process. This causes distortions that compromise the reconstruction process significantly, rendering the whole acquisition procedure useless, or in the best case requiring strenuous assisted editing tasks in order to obtain viable results. In this work we propose a method for detecting and filtering noisy zones in meshes generated through point clouds acquired from in-situ scanning southern elephant seals in their natural habitat. We trained a CNN model with meshes resulting from noisy and clean acquisitions. The trained neural network is able to filter, in subsequent acquisitions, those parts in the mesh that do not belong to the original objects. This greatly reduces or eliminates the amount of manual editing work that is required in order to obtain a useful acquisition.
机译：来自不同来源的无法控制的噪声的出现在扫描3D对象中具有几个困难。在野外动物的情况下，这尤其难以管理，因为在采集过程中它们的运动是不可避免的。这导致扭曲损害重建过程的重建过程，使整个采集程序无用，或者在需要艰苦辅助编辑任务的最佳案例中以获得可行的结果。在这项工作中，我们提出了一种方法，用于检测和过滤通过在原位扫描南部大象密封件中获取的点云产生的网格中的噪声区域。我们培训了由噪声和清洁收购产生的网格的CNN模型。训练有素的神经网络能够在后续采集中过滤，这些部分不属于原始对象的网格中。这大大减少或消除了获得有用的收购所需的手动编辑工作量。

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