Three-dimensional shape segmentation method and system based on weight energy adaptive distribution

Abstract

The invention provides a three-dimensional shape segmentation method and system based on weight energy adaptive distribution. The method comprises the steps of training a deep neural network and performing a segmentation prediction process on a to-be-segmented three-dimensional model, the training process comprising the steps of: segmenting the three-dimensional model into n small blocks, randomlyselecting a triangular patch on each small block to represent the small block, and determining a segmentation label corresponding to each triangular patch through a segmentation label; extracting a feature vector of each triangular patch; calculating the minimum value of the geodesic distances of the triangular patches under the same three-dimensional model through the segmentation labels to obtain weight energy distribution, calculating and obtaining the soft label of each triangular patch, and taking the soft labels of the triangular patches under all three-dimensional models as the outputof deep neural network training; and training a deep neural network with a random inactivation layer by using the input and the output. The method has the advantages of high accuracy, strong robustness, strong learning expansion capability and the like.

Description

technical field [0001] The invention relates to the field of three-dimensional image segmentation, in particular to a three-dimensional shape segmentation method and system based on weight energy adaptive distribution. Background technique [0002] With the continuous development of 3D scanning technology and modeling technology, 3D models are widely used in actual production and life and scientific research, and the research on related digital geometry processing technology has also been deepened. Among them, the 3D model segmentation algorithm is the basis of many digital geometry processing technologies, such as mesh deformation editing, model skeleton extraction and shape retrieval, etc., can all be used in model segmentation algorithms. The rapid growth of the types and quantities of 3D models puts forward higher requirements on the performance of model segmentation algorithms. [0003] In recent years, many scholars at home and abroad have done a lot of research on th...

AI Technical Summary

Problems solved by technology

However, the traditional fully supervised learning algorithm uses split digital labels for training, which reduces the learning ability of the neural network, resulting in poor prediction performance of the neural network at the edge of the segmentation, and the loss function is difficult to decrease.

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