  There has been a growing adoption of computer vision tools and technologies
in architectural design workflows over the past decade. Notable use cases
include point cloud generation, visual content analysis, and spatial awareness
for robotic fabrication. Multiple image classification, object detection, and
semantic pixel segmentation models have become popular for the extraction of
high-level symbolic descriptions and semantic content from two-dimensional
images and videos. However, a major challenge in this regard has been the
extraction of high-level architectural structures (walls, floors, ceilings
windows etc.) from diverse imagery where parts of these elements are occluded
by furniture, people, or other non-architectural elements. This project aims to
tackle this problem by proposing models that are capable of extracting
architecturally meaningful semantic descriptions from two-dimensional scenes of
populated interior spaces. 1000 virtual classrooms are parametrically
generated, randomized along key spatial parameters such as length, width,
height, and door/window positions. The positions of cameras, and
non-architectural visual obstructions (furniture/objects) are also randomized.
A Generative Adversarial Network (GAN) for image-to-image translation (Pix2Pix)
is trained on synthetically generated rendered images of these enclosures,
along with corresponding image abstractions representing high-level
architectural structure. The model is then tested on unseen synthetic imagery
of new enclosures, and outputs are compared to ground truth using pixel-wise
comparison for evaluation. A similar model evaluation is also carried out on
photographs of existing indoor enclosures, to measure its performance in
real-world settings.
