Point2FFD: Learning Shape Representations of Simulation-Ready 3D Models for Engineering Design Optimization

International Conference on 3D Vision (3DV), pp. 1024–1033

Abstract

Methods for learning on 3D point clouds became ubiquitous due to the popularization of 3D scanning technology and advances of machine learning techniques. Among these methods, point-based deep neural networks have been utilized to explore 3D designs in optimization tasks. However, engineering computer simulations require high-quality meshed models, which are challenging to automatically generate from unordered point clouds. In this work, we propose Point2FFD: A novel deep neural network for learning compact geometric representations and generating simulation-ready meshed models. Built upon an autoencoder architecture, Point2FFD learns to compress 3D point clouds into a latent design space, from which the network generates 3D polygonal meshes by selecting and deforming simulation-ready mesh templates. Through benchmark experiments, we show that our proposed network achieves comparable shape-generative performance than existing state-of-the-art point-based generative models. In real world-inspired vehicle aerodynamic optimizations, we demonstrate that Point2FFD generates simulation-ready meshes of realistic car shapes and leads to better optimized designs than the benchmarked networks.

Authors 5

1. Thiago Rios
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany

2. Bas van Stein Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), CA Leiden, The Netherlands

3. Thomas Bäck Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), CA Leiden, The Netherlands

4. Bernhard Sendhoff
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany

5. Stefan Menzel
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany