PlankAssembly Dataset
The PlankAssembly Dataset consists of 26,707 shape programs derived from parametric CAD models.
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.
A cabinet is typically assembled by a list of plank models, where each plank is represented as an axis-aligned cuboid. A cuboid has six degrees of freedom, which correspond to the starting and ending coordinates along the three axes:
Cuboid(xmin, ymin, zmin, xmax, ymax, zmax).The PlankAssembly Dataset consists of 26,707 shape programs derived from parametric CAD models.
Scaling along x-axis
Scaling along z-axis
Moving a single plank model
@inproceedings{PlankAssembly,
author = {Hu, Wentao and Zheng, Jia and Zhang, Zixin and Yuan, Xiaojun and Yin, Jian and Zhou, Zihan},
title = {PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs},
booktitle = {ICCV},
year = {2023}
}