Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation

Abstract:


Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits, boundary preservation and smoothness. Moreover, Pano3D moves beyond typical intra-dataset evaluation to inter-dataset performance assessment. By disentangling the capacity to generalize in unseen data into different test splits, Pano3D represents a holistic benchmark for 360 degree depth estimation. We use it as a basis for an extended analysis seeking to offer insights into classical choices for depth estimation. This results into a solid baseline for panoramic depth that followup works can built upon to steer future progress.


  • G. Albanis, N. Zioulis, P. Drakoulis, V. Gkitsas, V. Sterzentsenko, F. Alvarez, D. Zarpalas, P. Daras, "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June, 2021.

  • Visual Computing Lab

    The focus of the Visual Computing Laboratory is to develop new algorithms and architectures for applications in the areas of 3D processing, image/video processing, computer vision, pattern recognition, bioinformatics and medical imaging.

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    Contact Information

    Dr. Petros Daras, Research Director
    6th km Charilaou – Thermi Rd, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras(at)iti(dot)gr