Existing 3D model reconstruction methods typically produce outputs in the
form of voxels, point clouds, or meshes. However, each of these approaches has
its limitations and may not be suitable for every scenario. For instance, the
resulting model may exhibit a rough surface and distorted structure, making
manual editing and post-processing challenging for humans. In this paper, we
introduce a novel 3D reconstruction method designed to address these issues. We
trained a visual transformer to predict a "scene descriptor" from a single
wire-frame image. This descriptor encompasses crucial information, including
object types and parameters such as position, rotation, and size. With the
predicted parameters, a 3D scene can be reconstructed using 3D modeling
software like Blender or Rhino Grasshopper which provides a programmable
interface, resulting in finely and easily editable 3D models. To evaluate the
proposed model, we created two datasets: one featuring simple scenes and
another with complex scenes. The test results demonstrate the model's ability
to accurately reconstruct simple scenes but reveal its challenges with more
complex ones.

本文介绍了一种新颖的 3D 重建方法，利用视觉转换器从单一线框图中预测 “场景描述符”，该描述符包含了关键信息，如物体类型、位置、旋转和大小参数，通过使用类似 Blender 或 Rhino Grasshopper 的可编程界面的 3D 建模软件，可以用预测的参数重建 3D 场景，得到易于编辑的精细 3D 模型。实验结果表明，该模型能够准确地重建简单场景，但在复杂场景中存在挑战。