2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2021
DOI: 10.1109/cvprw53098.2021.00413
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Pano3D: A Holistic Benchmark and a Solid Baseline for 360° Depth Estimation

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Cited by 32 publications
(17 citation statements)
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“…For our analysis we use a dense regression task, namely depth estimation, which requires the balancing of both boundary preservation and smoothness of the predicted depth maps, apart from its direct depth estimation performance. To fully exploit rich depth maps that include both smooth regions, as well as lots of foreground to background depth discontinuities, we use an omnidirectional image benchmark [1]. It includes spherical panoramas that capture entire indoor scenes, containing a lot of flat surfaces (ceiling, floors, tables, etc.…”
Section: Resultsmentioning
confidence: 99%
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“…For our analysis we use a dense regression task, namely depth estimation, which requires the balancing of both boundary preservation and smoothness of the predicted depth maps, apart from its direct depth estimation performance. To fully exploit rich depth maps that include both smooth regions, as well as lots of foreground to background depth discontinuities, we use an omnidirectional image benchmark [1]. It includes spherical panoramas that capture entire indoor scenes, containing a lot of flat surfaces (ceiling, floors, tables, etc.…”
Section: Resultsmentioning
confidence: 99%
“…Our implementation is based on moai [37] which uses PyTorch 1.8 [40], PyTorch Lightning 1.0.7 [11] and Kornia 0.4.1 [45]. For all experiments we use the same UNet architecture and supervision scheme used in Pano3D [1], fixing the learning rate (0.0002), optimizer (default parameterized Adam [25]), batch size (4) and random number generator seed. Thus, only the skip connection varies from experiment to experiment.…”
Section: Resultsmentioning
confidence: 99%
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“…Similar to the previous works, we estimate our method with the commonly used error metrics: MAE, MRE, RMSE, RMSE log scale invariant, and δ t , t ∈ 1.25, 1.25 2 , 1.25 3 . Specially, the metrics of dbe acc , dbe comp , prec t , and rec t , t ∈ 0.25, 0.5, 1 provided by Pano3D [43] are employed to evaluate the capability of boundary preservation of models.…”
Section: B Quantitative and Qualitative Evaluationsmentioning
confidence: 99%
“…Using domain adaptation for synthetic data requires both large-scale 3D models with great variations and corresponding similar 360-degree color ground truth. Most concurrent works [62] [56] either use synthetic datasets (i.e., PanoSunCG [55]) or 3D scanned datasets (i.e., Matterport3D [7], Stanford 2D-3D [2], Pano3D [1]). The former is generated with 3D models and a virtual omnidirectional camera without domain adaptation, and the latter ones are captured with specialized equipment and post-processed.…”
Section: Data In Single-view Depth Estimationmentioning
confidence: 99%