DeepCAD: A Deep Generative Network for Computer-Aided Design Models (2105.09492v2)

Abstract: Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic.

• The paper introduces a transformer-based model that generates procedural CAD designs by modeling sequences of CAD operations.
• It employs continuous embedding and quantization techniques to accurately capture both command types and their parameters.
• Evaluation results show high command and parameter accuracy with low invalid topology rates, supporting efficient iterative design.

Overview of "DeepCAD: A Deep Generative Network for Computer-Aided Design Models"

The paper introduces DeepCAD, a novel generative model designed specifically for computer-aided design (CAD) models. Unlike traditional approaches in 3D shape generation which focus predominantly on discrete representations such as voxels, point clouds, or meshes, DeepCAD initiates a shift towards generative modeling of CAD designs, structured fundamentally on sequences of CAD operations. This is significant as CAD models encapsulate the procedural essence of shape creation, reflecting a more authentic representation employed across a variety of industrial applications including automotive, aerospace, and manufacturing design.

Technical Contributions

Drawing inspiration from natural language processing, the authors leverage the architecture of transformer networks to handle the complex sequential and parametric nature inherent in CAD model generation. The paper presents a method where CAD operations are likened to linguistic constructs, enabling the application of a transformer-based autoencoder to embed CAD models into a latent space and subsequently decode them. A distinctive feature of their approach includes the creation of a new dataset comprising 178,238 CAD models and their respective construction sequences, facilitating robust training and public research opportunities.

Key technical innovations include:

• A structured representation of CAD operations as sequential commands with parameters.
• Utilization of continuous embedding strategies to capture both command types and their parameters.
• Adoption of quantization techniques to convert continuous parameters into discrete levels, enhancing geometric relation respect.

Evaluation Metrics

The paper evaluates the proposed model via autoencoding performance, assessing metrics such as Command Accuracy, Parameter Accuracy, and Chamfer Distance. Across various ablation studies, significant conclusions were drawn supporting their design choices like absolute position representation for curves and parameter quantization.

Quantitative results suggest that the model achieves high accuracy in recovering operation sequences, boasting commendable scores for both command and parameter accuracy while maintaining low invalid topology ratios. Compared against alternative approaches, DeepCAD demonstrates favorable performance especially in handling longer CAD command sequences.

Implications and Future Work

The implications of this research are multifaceted. Practically, the generative model for CAD allows designers and engineers to generate editable CAD models efficiently, supporting iterative design processes. From a theoretical perspective, it opens new avenues for research in procedural generation using deep learning, highlighting the potential for integrating more diverse CAD operations and overcoming challenges with sequence validity.

The paper also underscores the potential for reconstructive uses, such as converting scanned 3D point clouds back into CAD models, thereby bridging the gap between raw data acquisition and polished design processes.

Future research directions include extending DeepCAD's capabilities to accommodate complex operations like fillet, which require direct interactions with the model's boundary representation, and further enhancing the model's generalization abilities to cater across variety designs and operations.

Conclusion

DeepCAD represents a pioneering step in the field of CAD model generation. By successfully translating principles from sequence modeling to handle CAD operations, the work laid foundational groundwork for subsequent explorations in computational design and engineering. The provision of a large-scale dataset and public accessibility further enriches the landscape for sustained innovations and development in this focal area.