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D3.2 AI-based Indoor Scene Understanding v2

Vladimiros Sterzentsenko; Georgios Albanis; Nikolaos Zioulis; Vasileios Gkitsas; Antonis Karakottas; Petros Drakoulis; Pachalina Medentzidou; Alexandros Doumanoglou; Dimitrios Zarpalas; Werner Bailer; Stefanie Onsori-Wechtitsch; Hermann Fürntratt

This deliverable documents the approaches for designing and developing the data-driven models that drive
the ATLANTIS AI services backend. Their basic principle of operation is monocular inference from spherical
panoramas which means that depending on the task at hand, these models will need to provide solutions for
challenging, ill-posed problems. Yet the recent developments in data-driven models and the expanded
availability of data are the drivers of such emerging services, upon which innovative tools and applications
like the ATLANTIS authoring tool can be developed. In this updated version, the improved results from WP3
are presented which include works for scene layout and depth estimation, semantic segmentation, and a
novel generative approach for Diminished Reality applied at spherical panoramas. These services have been
integrated into the ATLANTIS application and assessed in the validation. Note that the deployment-related
aspects (e.g., APIs, registration of the AR scene with the real room) are described in D4.2.

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