GED-Net: A Multi-modal Reconstruction Network for CAD Parametric Models based on Grouped Entity Decoding

Optical and photonic computing systems offer a high-performance, energy-efficient paradigm for next-generation AI hardware, but their scaling hinges on cross-layer hardware-algorithm co-design and advanced automation tools, where CAD modeling is indispensable. Reconstructing parametric CAD models from generic 3D data, such as point clouds, is a problem of practical significance, as it allows non-editable geometry to be modified and reused. However, the current mainstream approach for CAD reconstruction faces two principal challenges: (1) inherent structural defects in sequential command representations, and (2) neglecting the complementary information across multiple modalities leads to incomplete feature extraction in deep learning. To overcome these limitations, we employ a multi-modal reconstruction network. It integrates the point cloud with its rendered multi-view images as the input information, with a lightweight similarity gating module dynamically fusing features of these two modalities. To address the structural defects, we propose a novel grouped entity structure, with a decoder which separately decodes extrusion entities and corresponding sketches in two stages. Experiments demonstrate that our method achieves a reconstruction Chamfer Distance of 0.002 and reduces the inefficiency rate to 5.49% on about 8,000 test samples of the standard dataset, which are 1/4 and 2/5 of those achieved by the baseline method, respectively. More importantly, we develop an end-to-end practical pipeline that automatically translates the network’s output into fully editable parametric models within industrial CAD software (CATIA V5). This bridge from deep learning to application demonstrates the strong practical value of our work. The code is available at https://gitlink.org.cn/fzhe/GEDNet.