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


Thiago Rios, Bas van Stein, Thomas B├Ąck, Bernhard Sendhoff and Stefan Menzel


Recently, methods for learning on 3D point cloud data became ubiquitous due to the popularization of 3D scanning technology and advances of machine learning techniques. Among these methods, point-based shape-generative deep neural networks have been utilized for exploring and optimizing 3D designs. However, most of the computer simulation methods in engineering require high-quality meshed models, which are still 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 cloud data into a latent design space, from which the network generates 3D polygonal meshes by selecting and deforming simulation-ready mesh templates. For a set of benchmark experiments, we show that our proposed network achieves comparable shape-generative performance than existing state-of-the-art engineering deep-generative models. In real world-inspired vehicle aerodynamic optimizations, we also demonstrate that Point2FFD generates simulation-ready meshes with higher fidelity than the benchmark networks and, thus, improve the quality of the optimal results.

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  Important Dates

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Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021