Today's architectural engineering and construction (AEC) software require a
learning curve to generate a three-dimension building representation. This
limits the ability to quickly validate the volumetric implications of an
initial design idea communicated via a single sketch. Allowing designers to
translate a single sketch to a 3D building will enable owners to instantly
visualize 3D project information without the cognitive load required. If
previous state-of-the-art (SOTA) data-driven methods for single view
reconstruction (SVR) showed outstanding results in the reconstruction process
from a single image or sketch, they lacked specific applications, analysis, and
experiments in the AEC. Therefore, this research addresses this gap,
introducing a deep learning method: Vitruvio. Vitruvio adapts Occupancy Network
for SVR tasks on a specific building dataset (Manhattan 1K). This adaptation
brings two main improvements. First, it accelerates the inference process by
more than 26\% (from 0.5s to 0.37s). Second, it increases the reconstruction
accuracy (measured by the Chamfer Distance) by 18\%. During this adaptation in
the AEC domain, we evaluate the effect of the building orientation in the
learning procedure since it constitutes an important design factor. While
aligning all the buildings to a canonical pose improved the overall
quantitative metrics, it did not capture fine-grain details in more complex
building shapes (as shown in our qualitative analysis). Finally, Vitruvio
outputs a 3D-printable building mesh with arbitrary topology and genus from a
single perspective sketch, providing a step forward to allow owners and
designers to communicate 3D information via a 2D, effective, intuitive, and
universal communication medium: the sketch.

本研究提出了一种基于深度学习方法的建筑单视图重建方法 - Vitruvio，通过该方法可以通过单个草图生成三维建筑模型，有效地解决了在建筑工程和施工软件中的制约问题。

Vitruvio: 通过单点透视草图生成 3D 建筑网格

Vitruvio: 3D Building Meshes via Single Perspective Sketches

Inspired by the recent success of methods that employ shape priors to achieve
robust 3D reconstructions, we propose a novel recurrent neural network
architecture that we call the 3D Recurrent Reconstruction Neural Network
(3D-R2N2). The network learns a mapping from images of objects to their
underlying 3D shapes from a large collection of synthetic data. Our network
takes in one or more images of an object instance from arbitrary viewpoints and
outputs a reconstruction of the object in the form of a 3D occupancy grid.
Unlike most of the previous works, our network does not require any image
annotations or object class labels for training or testing. Our extensive
experimental analysis shows that our reconstruction framework i) outperforms
the state-of-the-art methods for single view reconstruction, and ii) enables
the 3D reconstruction of objects in situations when traditional SFM/SLAM
methods fail (because of lack of texture and/or wide baseline).

使用深度学习方法，构建一个名为 3D-R2N2 的递归神经网络结构，使用大量的合成数据将图像映射到对应的 3D 形状，无需图像注释或对象分类标签，可以在缺乏纹理或宽基线的情况下，实现对象的 3D 重建，并在单视图重建方面优于现有的最先进方法。