Sketch2CAD: 3D CAD Model Reconstruction from 2D Sketch using Visual Transformer
It addresses the problem of creating editable 3D models for CAD users, though it is incremental as it builds on existing transformer methods for a specific bottleneck.
The paper tackles 3D reconstruction from 2D sketches by generating CAD-compatible models using a visual transformer, achieving accurate results on simple scenes but facing difficulties with complex ones.
Current 3D reconstruction methods typically generate outputs in the form of voxels, point clouds, or meshes. However, each of these formats has inherent limitations, such as rough surfaces and distorted structures. Additionally, these data types are not ideal for further manual editing and post-processing. In this paper, we present a novel 3D reconstruction method designed to overcome these disadvantages by reconstructing CAD-compatible models. We trained a visual transformer to predict a "scene descriptor" from a single 2D wire-frame image. This descriptor includes essential information, such as object types and parameters like position, rotation, and size. Using the predicted parameters, a 3D scene can be reconstructed with 3D modeling software that has programmable interfaces, such as Rhino Grasshopper, to build highly editable 3D models in the form of B-rep. To evaluate our proposed model, we created two datasets: one consisting of simple scenes and another with more complex scenes. The test results indicate the model's capability to accurately reconstruct simple scenes while highlighting its difficulties with more complex ones.