CAD-Recode：從點雲進行CAD代碼的逆向工程

摘要

通常，計算機輔助設計（CAD）模型是通過依序繪製參數化草圖並應用CAD操作來獲得3D模型的。3D CAD反向工程問題包括從諸如點雲之類的3D表示中重建草圖和CAD操作序列。本文通過在CAD序列表示、網絡設計和數據集三個層面上進行新穎貢獻來應對這一挑戰。特別地，我們將CAD草圖拉伸序列表示為Python代碼。提出的CAD-Recode將點雲轉換為Python代碼，當執行時，可以重建CAD模型。利用預先訓練的大型語言模型（LLMs）對Python代碼的曝光，我們將一個相對較小的LLM作為CAD-Recode的解碼器，並將其與輕量級點雲投影儀結合。CAD-Recode僅在一個提出的包含一百萬個多樣CAD序列的合成數據集上進行訓練。CAD-Recode在三個數據集上明顯優於現有方法，同時需要較少的輸入點。值得注意的是，在DeepCAD和Fusion360數據集上，其平均Chamfer距離比最先進方法低10倍。此外，我們展示我們的CAD Python代碼輸出可被現成的LLMs解釋，從而實現CAD編輯和從點雲提問CAD特定問題。

English

Computer-Aided Design (CAD) models are typically constructed by sequentially drawing parametric sketches and applying CAD operations to obtain a 3D model. The problem of 3D CAD reverse engineering consists of reconstructing the sketch and CAD operation sequences from 3D representations such as point clouds. In this paper, we address this challenge through novel contributions across three levels: CAD sequence representation, network design, and dataset. In particular, we represent CAD sketch-extrude sequences as Python code. The proposed CAD-Recode translates a point cloud into Python code that, when executed, reconstructs the CAD model. Taking advantage of the exposure of pre-trained Large Language Models (LLMs) to Python code, we leverage a relatively small LLM as a decoder for CAD-Recode and combine it with a lightweight point cloud projector. CAD-Recode is trained solely on a proposed synthetic dataset of one million diverse CAD sequences. CAD-Recode significantly outperforms existing methods across three datasets while requiring fewer input points. Notably, it achieves 10 times lower mean Chamfer distance than state-of-the-art methods on DeepCAD and Fusion360 datasets. Furthermore, we show that our CAD Python code output is interpretable by off-the-shelf LLMs, enabling CAD editing and CAD-specific question answering from point clouds.

CAD-Recode：從點雲進行CAD代碼的逆向工程

CAD-Recode: Reverse Engineering CAD Code from Point Clouds

摘要

Summary

Support