PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs
This addresses the challenge of robust 3D reconstruction from imperfect human-drawn designs, which is incremental by improving accuracy in noisy conditions.
The paper tackles the problem of converting 2D line drawings from three orthographic views into 3D CAD models, and the result is a method that significantly outperforms existing ones in handling noisy or incomplete inputs, as shown in experiments on a new benchmark dataset.
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.