CADTalk: An Algorithm and Benchmark for Semantic Commenting of CAD Programs

TL;DR

This paper introduces CADTalk, a novel method combining program parsing and visual-semantic analysis to generate semantic comments for CAD programs, supported by a new benchmark dataset and achieving over 83% accuracy.

Contribution

It presents a new approach for semantic commenting of CAD programs using multi-modal analysis and provides a comprehensive benchmark dataset for future research.

Findings

Achieved 83.24% accuracy on the CADTalk dataset.

Outperformed GPT-based and shape segmentation baselines.

Demonstrated effectiveness of combining program execution with visual analysis.

Abstract

CAD programs are a popular way to compactly encode shapes as a sequence of operations that are easy to parametrically modify. However, without sufficient semantic comments and structure, such programs can be challenging to understand, let alone modify. We introduce the problem of semantic commenting CAD programs, wherein the goal is to segment the input program into code blocks corresponding to semantically meaningful shape parts and assign a semantic label to each block. We solve the problem by combining program parsing with visual-semantic analysis afforded by recent advances in foundational language and vision models. Specifically, by executing the input programs, we create shapes, which we use to generate conditional photorealistic images to make use of semantic annotators for such images. We then distill the information across the images and link back to the original programs to semantically comment on them. Additionally, we collected and annotated a benchmark dataset, CADTalk, consisting of 5,288 machine-made programs and 45 human-made programs with ground truth semantic comments. We extensively evaluated our approach, compared it to a GPT-based baseline, and an open-set shape segmentation baseline, and reported an 83.24% accuracy on the new CADTalk dataset. Code and data: https://enigma-li.github.io/CADTalk/.