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Pano3D: GibsonV2 Full High Resolution

Albanis, Georgios; Zioulis, Nikolaos; Drakoulis, Petros; Gkitsas, Vasileios; Sterzentsenko, Vladimiros; Alvarez, Federico; Zarpalas, Dimitrios; Daras, Petros


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    <dcat:keyword>Spherical Depth Estimation</dcat:keyword>
    <dcat:keyword>Spherical Panoramas</dcat:keyword>
    <dcat:keyword>Omnidirectional Dataset</dcat:keyword>
    <dcat:keyword>Benchmark</dcat:keyword>
    <dcat:keyword>360</dcat:keyword>
    <dcat:keyword>Computer Vision</dcat:keyword>
    <dcat:keyword>Deep Learning</dcat:keyword>
    <dcat:keyword>Data-driven Methods</dcat:keyword>
    <dcat:keyword>3D Vision</dcat:keyword>
    <dcat:keyword>Depth Estimation</dcat:keyword>
    <dcat:keyword>Geometry Estimation</dcat:keyword>
    <dcat:keyword>Surface Orientation Estimation</dcat:keyword>
    <dcat:keyword>Indoor Scenes</dcat:keyword>
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    <dct:description>&lt;p&gt;Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. To achieve that we generate a new dataset and integrate evaluation metrics that capture not only depth performance, but also secondary traits like boundary preservation and smoothness. Moreover,&amp;nbsp;Pano3D&amp;nbsp;takes a step beyond typical intra-dataset evaluation schemes to inter-dataset performance assessment. By disentangling generalization to three different axes,&amp;nbsp;Pano3D&amp;nbsp;facilitates proper extrapolation assessment under different out-of-training data conditions. Relying on the&amp;nbsp;Pano3D&amp;nbsp;holistic benchmark for 360 depth estimation we perform an extended analysis and derive a solid baseline for the task.&lt;/p&gt;</dct:description>
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