Generation of computer-aided design (CAD) models from multi-view images may
be useful in many practical applications. To date, this problem is usually
solved with an intermediate point-cloud reconstruction and involves manual work
to create the final CAD models. In this contribution, we present a novel
network for an automated reverse engineering task. Our network architecture
combines three distinct stages: A convolutional neural network as the encoder
stage, a multi-view pooling stage and a transformer-based CAD sequence
generator.
The model is trained and evaluated on a large number of simulated input
images and extensive optimization of model architectures and hyper-parameters
is performed. A proof-of-concept is demonstrated by successfully reconstructing
a number of valid CAD models from simulated test image data. Various accuracy
metrics are calculated and compared to a state-of-the-art point-based network.
Finally, a real world test is conducted supplying the network with actual
photographs of two three-dimensional test objects. It is shown that some of the
capabilities of our network can be transferred to this domain, even though the
training exclusively incorporates purely synthetic training data. However to
date, the feasible model complexity is still limited to basic shapes.

计算机辅助设计（CAD）模型生成是一项重要任务，提出了一种利用卷积神经网络、多视角池化和基于转换器的 CAD 序列生成器的网络架构，通过大量的模拟输入图片进行训练和优化，成功地从测试图像数据重建有效的 CAD 模型，并表明该网络的一些能力可以迁移到实际照片领域，然而目前可行的模型复杂度仍然受限于基本形状。