论文标题
使用定向边界框和LSTM自动编码器分析和预测3D形状的变形3D形状
Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders
论文作者
论文摘要
对于复杂3D形状的序列，我们提出了一种通用方法，用于检测其分析模式，并通过使用复杂形状的结构成分来预测变形。我们将较长的短期存储器（LSTM）层纳入自动编码器，以创建低维表示，从而允许检测数据中的模式，并在变形行为中检测到时间动力学。这是通过两个解码器来实现的，一个用于重建，一个用于预测序列的未来时间步骤。在预处理步骤中，研究对象的组件被转换为方向的边界框，以捕获塑性变形的影响并降低描述结构的数据的维度。该体系结构对具有133个不同组件的模型的196个车祸模拟的结果进行了测试，其中材料属性是不同的。在潜在表示中，我们可以检测到不同组件的塑性变形中的模式。预测的边界框给出了最终模拟结果的估计，并且与不同的基准相比，它们的质量得到了提高。
For sequences of complex 3D shapes in time we present a general approach to detect patterns for their analysis and to predict the deformation by making use of structural components of the complex shape. We incorporate long short-term memory (LSTM) layers into an autoencoder to create low dimensional representations that allow the detection of patterns in the data and additionally detect the temporal dynamics in the deformation behavior. This is achieved with two decoders, one for reconstruction and one for prediction of future time steps of the sequence. In a preprocessing step the components of the studied object are converted to oriented bounding boxes which capture the impact of plastic deformation and allow reducing the dimensionality of the data describing the structure. The architecture is tested on the results of 196 car crash simulations of a model with 133 different components, where material properties are varied. In the latent representation we can detect patterns in the plastic deformation for the different components. The predicted bounding boxes give an estimate of the final simulation result and their quality is improved in comparison to different baselines.
