论文标题
对3D内部结构的调查预测其外部形状
A Survey On 3D Inner Structure Prediction from its Outer Shape
论文作者
论文摘要
对树木内部结构的分析对于森林专家，生物科学家和木材工业都非常重要。传统上，CT扫描仪被认为是获得树木准确内部表示的最有效方法。但是，此方法需要重要的投资，并降低了此操作的成本效益。我们的目标是设计基于神经网络的方法，以从其外部树皮形状预测树的内部密度。本文比较了不同的图像形象（2D），体积到体积（3D）和基于短期内存的卷积长期内存的神经网络体系结构，这是在从其外部树皮形状中预测树内缺陷分布的背景下。这些模型在树木内部密度及其相应的外表面的1800 CT扫描外观的体积结构的合成数据集上进行了训练。
The analysis of the internal structure of trees is highly important for both forest experts, biological scientists, and the wood industry. Traditionally, CT-scanners are considered as the most efficient way to get an accurate inner representation of the tree. However, this method requires an important investment and reduces the cost-effectiveness of this operation. Our goal is to design neural-network-based methods to predict the internal density of the tree from its external bark shape. This paper compares different image-to-image(2D), volume-to-volume(3D) and Convolutional Long Short Term Memory based neural network architectures in the context of the prediction of the defect distribution inside trees from their external bark shape. Those models are trained on a synthetic dataset of 1800 CT-scanned look-like volumetric structures of the internal density of the trees and their corresponding external surface.
