CAD-Recode: Reverse Engineering CAD Code from Point Clouds

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I whakaputaina i:	arXiv.org (Dec 18, 2024), p. n/a
Kaituhi matua:	Rukhovich, Danila
Ētahi atu kaituhi:	Dupont, Elona, Mallis, Dimitrios, Cherenkova, Kseniya, Anis Kacem, Aouada, Djamila
I whakaputaina:	Cornell University Library, arXiv.org
Ngā marau:	Datasets Drawing Image reconstruction Large language models Reverse engineering Three dimensional models Python Chamfering Computer aided design > CAD Network design Sketches Representations Synthetic data
Urunga tuihono:	Citation/Abstract Full text outside of ProQuest
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Whakaahuatanga
Whakarāpopotonga:	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.
ISSN:	2331-8422
Puna:	Engineering Database