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Hierarchical Neural Coding for Controllable CAD Model Generation (2307.00149v1)

Published 30 Jun 2023 in cs.CV and cs.LG

Abstract: This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neural codes, from global part arrangement down to local curve geometry; and 2) controls the generation or completion of CAD models by specifying the target design using a code tree. Concretely, a novel variant of a vector quantized VAE with "masked skip connection" extracts design variations as neural codebooks at three levels. Two-stage cascaded auto-regressive transformers learn to generate code trees from incomplete CAD models and then complete CAD models following the intended design. Extensive experiments demonstrate superior performance on conventional tasks such as random generation while enabling novel interaction capabilities on conditional generation tasks. The code is available at https://github.com/samxuxiang/hnc-cad.

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Citations (22)
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Summary

  • The paper introduces a hierarchical neural coding scheme using a variant of VQ-VAE with masked skip connections to capture design patterns in CAD models.
  • It employs two-stage cascaded auto-regressive transformers to predict design intent and generate or complete CAD models with user-guided control.
  • Experimental results show superior realism, diversity, and quality compared to state-of-the-art systems such as DeepCAD and SkexGen.

Hierarchical Neural Coding for Controllable CAD Model Generation

The paper introduces a novel approach to the generation of Computer Aided Design (CAD) models through a hierarchical neural coding scheme. The work addresses the need for design intent-aware generation and modification of CAD models, which traditional parametric CAD systems struggle to maintain when models are modified. This is achieved using a multi-level neural representation, which captures CAD design patterns from global part arrangements to local geometries. A variant of vector quantized variational autoencoders (VQ-VAE) with masked skip connections extracts these patterns as discrete neural codebooks at three hierarchical levels: solid, profile, and loop. The system utilizes two-stage cascaded auto-regressive transformers, enabling the generation and auto-completion of CAD shapes while respecting the hierarchical design intent.

Key Contributions

  1. Hierarchical Neural Code Representation: The framework encodes CAD models into a three-level hierarchical tree, leveraging VQ-VAE to create separate codebooks capturing solid, profile, and loop structures. Each level of the hierarchy naturally mirrors the design steps commonly followed in CAD modeling: from macro-level solid part arrangements to micro-level geometric curves.
  2. Masked Skip Connection in VQ-VAE: A modified VQ-VAE architecture incorporates a masked skip connection, mitigating the tendency of the decoder to directly reconstruct input details, thereby fostering a more abstract, design pattern-focused learning in the codebooks.
  3. Two-Stage Cascaded Transformers for Controllable Generation: With the aid of two decoupled auto-regressive transformers, the model first predicts the hierarchical design intent via codebooks and then generates or completes CAD models in alignment with this intended design. This enables user-guided modifications at any level of the design hierarchy, offering direct and intuitive control over both global and local design features.

Numerical and Empirical Evaluation

The experimental results underscore the effective generation of CAD models with enhanced realism, diversity, and quality compared to state-of-the-art systems such as DeepCAD and SkexGen. The model achieves superior scores in quantitative metrics: Coverage (COV), Minimum Matching Distance (MMD), Jensen-Shannon Divergence (JSD), along with higher unique and novelty rates. Human evaluations further affirm the model's competency in generating realistic and complex CAD designs indistinguishable from real, hand-crafted models.

Implications and Future Directions

The research significantly facilitates interactions in CAD design by allowing users not only design creation but also seamless modification and completion of designs. This represents a step towards design systems that incorporate and respect designer intent, thereby offering high-quality interactive design tools. Looking forward, one avenue for development is the integration of additional CAD operations beyond sketch-and-extrude, such as revolve or sweep, to offer even broader coverage of typical CAD design processes. The extension towards enforcing CAD validity via explicit geometric constraints or corrective post-processing methods is another promising direction to enhance the practical applicability of the system.

Overall, this work provides a scalable framework capable of supporting the design and iteration of complex CAD models, addressing both theoretical and practical needs within the engineering and design communities.

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