CAD-Recode: Reverse Engineering CAD Code from Point Clouds
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| Publicado no: | arXiv.org (Dec 18, 2024), p. n/a |
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| Autor principal: | |
| Outros Autores: | , , , , |
| Publicado em: |
Cornell University Library, arXiv.org
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| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full text outside of ProQuest |
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MARC
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| 001 | 3147266091 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3147266091 | ||
| 045 | 0 | |b d20241218 | |
| 100 | 1 | |a Rukhovich, Danila | |
| 245 | 1 | |a CAD-Recode: Reverse Engineering CAD Code from Point Clouds | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 18, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Datasets | ||
| 653 | |a Drawing | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Large language models | ||
| 653 | |a Reverse engineering | ||
| 653 | |a Three dimensional models | ||
| 653 | |a Python | ||
| 653 | |a Chamfering | ||
| 653 | |a Computer aided design--CAD | ||
| 653 | |a Network design | ||
| 653 | |a Sketches | ||
| 653 | |a Representations | ||
| 653 | |a Synthetic data | ||
| 700 | 1 | |a Dupont, Elona | |
| 700 | 1 | |a Mallis, Dimitrios | |
| 700 | 1 | |a Cherenkova, Kseniya | |
| 700 | 1 | |a Anis Kacem | |
| 700 | 1 | |a Aouada, Djamila | |
| 773 | 0 | |t arXiv.org |g (Dec 18, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3147266091/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.14042 |