Windowed-FourierMixer: Enhancing Clutter-Free Room Modeling with Fourier
  Transform
Bruno Henriques Benjamin Allaert Jean-Philippe Vandeborre
With the growing demand for immersive digital applications, the need to
understand and reconstruct 3D scenes has significantly increased. In this
context, inpainting indoor environments from a single image plays a crucial
role in modeling the internal structure of interior spaces as it enables the
creation of textured and clutter-free reconstructions. While recent methods
have shown significant progress in room modeling, they rely on constraining
layout estimators to guide the reconstruction process. These methods are highly
dependent on the performance of the structure estimator and its generative
ability in heavily occluded environments. In response to these issues, we
propose an innovative approach based on a U-Former architecture and a new
Windowed-FourierMixer block, resulting in a unified, single-phase network
capable of effectively handle human-made periodic structures such as indoor
spaces. This new architecture proves advantageous for tasks involving indoor
scenes where symmetry is prevalent, allowing the model to effectively capture
features such as horizon/ceiling height lines and cuboid-shaped rooms.
Experiments show the proposed approach outperforms current state-of-the-art
methods on the Structured3D dataset demonstrating superior performance in both
quantitative metrics and qualitative results. Code and models will be made
publicly available.