Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Hierarchical Neural Coding for Controllable CAD Model Generation
Authors: Xiang Xu, Pradeep Kumar Jayaraman, Joseph George Lambourne, Karl D.D. Willis, Yasutaka Furukawa
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,569 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,133 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate superior performance on conventional tasks such as unconditional generation while enabling novel interaction capabilities on conditional generation tasks. |
| Researcher Affiliation | Collaboration | 1Simon Fraser University, Canada 2Autodesk Research. Correspondence to: Xiang Xu <EMAIL>. |
| Pseudocode | No | The paper includes architectural diagrams (e.g., Figure 4) and descriptions of model components, but no formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | The code is available at https: //github.com/samxuxiang/hnc-cad. |
| Open Datasets | Yes | We use the large-scale Deep CAD dataset (Wu et al., 2021) with ground-truth sketch-and-extrude models. Deep CAD contains 178,238 sketch-and-extrude models with a split of 90% train, 5% validation, and 5% test samples. |
| Dataset Splits | Yes | Deep CAD contains 178,238 sketch-and-extrude models with a split of 90% train, 5% validation, and 5% test samples. |
| Hardware Specification | Yes | Models are trained on an Nvidia RTX A6000 GPU with a batch size of 256. |
| Software Dependencies | No | The paper mentions using the Adam W optimizer and Transformer backbones, but does not specify version numbers for any software, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | Input embedding dimension is 256. Feed-forward dimension is 512. Dropout rate is 0.1. Each Transformer network in the generation module has 6 layers with 8 attention heads. The codebook learning networks have 4 layers. We use the Adam W (Loshchilov & Hutter, 2018) optimizer with a learning rate of 0.001 after linear warm-up for 2000 steps. |