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METHODS FOR OBTAINING NORMAL VECTOR, GEOMETRY AND MATERIAL OF THREE-DIMENSIONAL OBJECTS BASED ON NEURAL NETWORK

机译:基于神经网络获得正常矢量,几何和材料的方法

摘要

A method for obtaining a normal vector, a geometry and a material of a three-dimensional object based on a neural network is provided. The present disclosure provides, based on an idea of “actively irradiating an object with a number of specific patterns, capturing photos at the same time, and obtaining a normal vector of an object by calculating the obtained photos”, an acquisition method combined with a neural network. Further, the method uses the obtained normal vector to optimize a model of the object. This method can also obtain material feature information while obtaining the normal vector. Finally, a high-quality geometric result and a high-quality material acquisition result are obtained jointly. The number of illumination patterns obtained by using this method is small, and the normal vector obtained by the method has high accuracy and the method is not limited to a specific acquisition device.
机译:提供了一种基于神经网络获得正常矢量的方法,几何形状和三维物体的材料。本公开基于“主动照射具有许多特定模式的对象,同时捕获照片,并通过计算所获得的照片”来获得对象的正常向量“,获取方法与A对象的正常向量相结合。神经网络。此外,该方法使用所获得的正常向量来优化对象的模型。该方法还可以在获得正常向量的同时获得材料特征信息。最后,共同获得了高质量的几何结果和高质量的材料采集结果。通过使用该方法获得的照明模式的数量小,并且通过该方法获得的正常向量具有高精度,并且该方法不限于特定的采集装置。

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