Discovering Design Concepts for CAD Sketches
Authors: Yuezhi Yang, Hao Pan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on large-scale sketch datasets [17]. The learned sketch concepts show that they provide modular interpretation of design sketches. The network can also be trained on incomplete input sketches and learn to auto-complete them. Comparisons with state-of-the-art approaches that solve sketch graph generation through autoregressive models show that the modular sketch concepts learned by our approach enable more accurate and interpretable completion results. |
| Researcher Affiliation | Collaboration | Yuezhi Yang The University of Hong Kong Microsoft Research Asia yzyang@cs.hku.hk Hao Pan Microsoft Research Asia haopan@microsoft.com |
| Pseudocode | No | The paper includes a "List 1" which defines a domain-specific language syntax, but it is not pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | We defer network details to the supplementary and open-source code and data to facilitate future research3. 3URL to code and data: https://github.com/yyuezhi/Sketch Concept |
| Open Datasets | Yes | Following previous works [6, 13, 18], we adopt the Sketch Graphs dataset [17] which contains millions of real-world CAD sketches for training and evaluation. [17] Ari Seff, Yaniv Ovadia, Wenda Zhou, and Ryan P. Adams. Sketch Graphs: A large-scale dataset for modeling relational geometry in computer-aided design. In ICML 2020 Workshop on Object-Oriented Learning, 2020. |
| Dataset Splits | No | We filter the data by removing trivially simple sketches and duplicates, and limit the sketch complexity such that the number of primitives and constraints is within [20, 50]. As a result, we obtain around 1 million sketches and randomly split them into 950k for training and 50k for testing. |
| Hardware Specification | No | The paper states: "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Please see Appendix." However, the provided text does not include the appendix, thus no specific hardware details are available in the main body. |
| Software Dependencies | No | The paper mentions "we implement the parameter network as a transformer decoder in a similar way as [2]" but does not specify software names with version numbers for reproducibility. |
| Experiment Setup | Yes | The entire model is trained end-to-end by reconstruction and modularity objectives. In particular, we design loss functions that measure differences between the generated and groundtruth sketch graphs, in terms of both per-element attributes and pairwise references. Given our explicit modeling of encapsulated structures of the learned concepts, we can further enhance the modularity of the generation by introducing a bias loss that encourages in-concept references. We denote the average loss of all generated terms as Lrecon. Ltotal = wrecon Lrecon + wsharp Lsharp + wvq Lvq + wbias Lbias, (7) where we empirically use weights wrecon = 1, wsharp = 20, wvq = 1, wbias = 25 throughout all experiments unless otherwise specified in the ablation studies. |