@inproceedings{aca6bdf5fe304a3aa1fcc1b3ed26520e,
title = "PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs",
abstract = "In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practice where the input drawings created by human designers are imperfect. To overcome this difficulty, we leverage the attention mechanism in a Transformer-based sequence generation model to learn flexible mappings between the input and output. Further, we design shape programs which are suitable for generating the objects of interest to boost the reconstruction accuracy and facilitate CAD modeling applications. Experiments on a new benchmark dataset show that our method significantly outperforms existing ones when the inputs are noisy or incomplete.",
author = "Wentao Hu and Jia Zheng and Zixin Zhang and Xiaojun Yuan and Jian Yin and Zihan Zhou",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 ; Conference date: 02-10-2023 Through 06-10-2023",
year = "2023",
doi = "10.1109/ICCV51070.2023.01695",
language = "English (US)",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "18449--18459",
booktitle = "Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023",
address = "United States",
}