cadrille: Multi-modal CAD Reconstruction with Reinforcement Learning

Abstract

Computer-Aided Design (CAD) plays a central role in engineering and manufacturing, making it possible to create precise and editable 3D models. Using a variety of sensor or user-provided data as inputs for CAD reconstruction can democratize access to design applications. However, most existing methods focus on a single input modality: point clouds, images, or texts, which limits their generalizability and robustness, while few multimodal approaches struggle to deliver competitive quality. Leveraging advances in vision-language models (VLM), we propose $\texttt{cadrille}$, a multimodal CAD reconstruction model that takes inputs of three modalities and outputs executable Python code for CAD reconstruction. Inspired by large language model (LLM) training paradigm, we adopt a two-stage pipeline: supervised fine-tuning (SFT) on large-scale procedurally generated data, followed by reinforcement learning (RL) fine-tuning using online feedback, obtained programatically. In the DeepCAD benchmark, our SFT model outperforms existing single-modal approaches in all three input modalities simultaneously. More importantly, after RL fine-tuning, $\texttt{cadrille}$ sets new state-of-the-art in as many as 10 benchmarks across three modalities and four datasets, including a real-world one.