We lifted 2 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| yyuezhi/sketchconcept | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| string_strip | Ran | yyuezhi/sketchconcept/plot_interactive_visualize.py pointer only (licence: NONE) · get_code("ed89421d51647ed5") |
| get_cmap | Not yet run | yyuezhi/sketchconcept/plot_interactive_visualize.py pointer only (licence: NONE) · get_code("ed37c72ac1938be8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Sketch design concepts are recurring patterns found in parametric CAD sketches. Though rarely explicitly formalized by the CAD designers, these concepts are implicitly used in design for modularity and regularity. In this paper, we propose a learning based approach that discovers the modular concepts by induction over raw sketches. We propose the dual implicit-explicit representation of concept structures that allows implicit detection and explicit generation, and the separation of structure generation and parameter instantiation for parameterized concept generation, to learn modular concepts by end-to-end training. We demonstrate the design concept learning on a large scale CAD sketch dataset and show its applications for design intent interpretation and auto-completion.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.14451")
get_code_for_paper("2210.14451")
have("2210.14451")
Connect an agent — have() is free.