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PAnoramic Semantic Segmentation (PASS) isanimportant task incomputer vision, as it enables semantic understanding of a 360 environment. Currently, most of existing works have focused on addressing the distortion issues in 2D panoramic images without considering spatial properties of indoor scene. This restricts PASS methods inperceiving contextual attributestodealwith theambiguity when working with monocular images. In this paper, we propose anovel approach for indoor panoramic semantic segmentation. Unlike previous works, we consider the panoramic image as a composition of segment groups:oversampled segments,representing planar structures suchasfloorsandceilings, and under-sampled segments, representing other scene elements.

Information (IGN) that provides a unique and rich resource for large-scale geospa-tial analysis.

Spatial graphs are particular graphs for which the nodes are localized in space (e.g., public transport network, molecules, branching biological structures). In this work, we consider the problem of spatial graph reduction, that aims to find a smaller spatial graph (i.e., with less nodes) with the same overall structure as the initial one. In this context, performing the graph reduction while preserving the main topological features of the initial graph is particularly relevant, due to the additional spatial information. Thus, we propose a topological spatial graph coarsening approach based on a new framework that finds a trade-off between the graph reduction and the preservation of the topological characteristics. The coarsening is realized by collapsing short edges. In order to capture the topological information required to calibrate the reduction level, we adapt the construction of classical topological descriptors made for point clouds (the so-called persistent diagrams) to spatial graphs. This construction relies on the introduction of a new filtration called triangle-aware graph filtration. Our coarsening approach is parameter-free and we prove that it is equivariant under rotations, translations and scaling of the initial spatial graph. We evaluate the performances of our method on synthetic and real spatial graphs, and show that it significantly reduces the graph sizes while preserving the relevant topological information.

Shah and Mäntylä [1995] from an unstructured 3D scan ( e.g .