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Generation of computer-aided design (CAD) models from multi-view images maybe useful in many practical applications. To date, this problem is usuallysolved with an intermediate point-cloud reconstruction and involves manual workto create the final CAD models. In this contribution, we present a novelnetwork for an automated reverse engineering task. Our network architecturecombines three distinct stages: A convolutional neural network as the encoderstage, a multi-view pooling stage and a transformer-based CAD sequencegenerator.  The model is trained and evaluated on a large number of simulated inputimages and extensive optimization of model architectures and hyper-parametersis performed. A proof-of-concept is demonstrated by successfully reconstructinga number of valid CAD models from simulated test image data. Various accuracymetrics are calculated and compared to a state-of-the-art point-based network.  Finally, a real world test is conducted supplying the network with actualphotographs of two three-dimensional test objects. It is shown that some of thecapabilities of our network can be transferred to this domain, even though thetraining exclusively incorporates purely synthetic training data. However todate, the feasible model complexity is still limited to basic shapes.

Automatic Reverse Engineering: Creating computer-aided design (CAD) models from multi-view images