SPIDER: A framework for processing, editing and presenting immersive high-resolution spherical indoor scenes

Muhammad Tukur, Giovanni Pintore, Enrico Gobbetti, Jens Schneider, Marco Agus
Graphical Models, Volume 128, page 101182:1-101182 - july 2023
Today's Extended Reality (XR) applications that call for specific Diminished Reality (DR) strategies to hide specific classes of objects are increasingly using 360-degree cameras, which can capture entire areas in a single picture. In this work, we present an interactive-based image processing, editing and rendering system named SPIDER, that takes a spherical 360-degree indoor scene as input. The system is composed of a novel integrated deep learning architecture for extracting geometric and semantic information of full and empty rooms, based on gated and dilated convolutions, followed by a super-resolution module for improving the resolution of the color and depth signals. The obtained high resolution representations allow users to perform interactive exploration and basic editing operations on the reconstructed indoor scene, namely: i) rendering of the scene in various modalities (point cloud, polygonal, wireframe) ii) refurnishing (transferring portions of rooms) iii) deferred shading through the usage of precomputed normal maps. These kinds of scene editing and manipulations can be used for assessing the inference from deep learning models and enable several Mixed Reality applications in areas such as furniture retails, interior designs, and real estates. Moreover, it can also be useful in data augmentation, arts, designs, and paintings. We report on the performance improvement of the various processing components on public domain spherical image indoor datasets.

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BibTex references

  author       = {Tukur, M. and Pintore, G. and Gobbetti, E. and Schneider, J. and Agus, M.},
  title        = {SPIDER: A framework for processing, editing and presenting immersive high-resolution spherical indoor scenes},
  journal      = {Graphical Models},
  volume       = {128},
  pages        = {101182:1-101182},
  month        = {july},
  year         = {2023},
  keywords     = {visual computing},
  doi          = {10.1016/j.gmod.2023.10118},
  url          = {},

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