... NYU - Depth Matlab V2 , Middlebury AOD - Net [ 11 ] 2017 NYU - Depth Pycaffe V2 RBF [ 6 ] 2018 Flickr , Matlab Google , Middlebury MLP [ 7 ] 2018 126 RGB Matlab Pictures , Middlebury LDT - Net [ 12 ] 2018 NYU - Depth TensorFlow V2 and ...
Object Detection In Adversarial Vision Mrinal Kanti Bhowmik. 4.1.7 NYU Depth V2 Dataset NYU Depth V2 [44] is another dataset that provides RGB-D images and dense annotations for various indoor scenes. It contains 1,449 RGB-D images ...
... NYU Depth V2 and Pascal VOC 2007, are split into images used for training and sequestered testing images. The NYU Depth V2 database is randomly split into training and testing scenes (with approximately half in each category). In the ...
... NYU Depth V2 datasets in Fig. 9. The NYU Depth V2 dataset has a different color property than the RRD Prambanan dataset. In the RRD Prambanan dataset, the types of colors displayed on the material differences are similar. That is why it ...
... depth values . For iBims - 1 benchmark [ 17 ] , we compute 5 similar metrics as for NYU - Depth - v2 plus the root mean square error in log - space ( log10 ) , planarity errors ( plan , orie ) , depth boundary errors ( acc , comp ) ...
... NYU - Depth V2 . We use NYU - Depth V2 [ 29 ] as the most important dataset for evaluation . Table 1 presents the performance comparison on the official test set of NYU - Depth V2 . Our proposed model shows the state - of - the - art ...
... NYU Depth v2 and 20∼60 for Make3D. For data augmentation, we follow the strategies proposed in [2] and the parameters are described here. The scaling factor s ∈ {1,1.2,1.5}, the rotation factor r ∈ [−5,5], and the color scaling ...
... Nyu-depth v2 dataset [20] to create the composite training image. Nyu-depth v2 dataset is composed of 1449 indoor color images with dense marks of real depth information. The original depth map has been projected and colored as [20] ...
... Depth completion results on the NYU Depth V2 dataset Method RMSE↓ S2D [1] 0.230 CSPN [2] 0.112 TWISE [3] 0.097 NLSPN [4] 0.092 Our 0.099 Oursmall 0.111 REL↓ δ1 ↑ δ2 ↑ δ3 ↑ MACs(G)↓ 0.044 97.1 99.4 99.8 9.3 0.016 99.6 99.9 100 ...
... depth maps of the framed region in the first column images. Our depth maps are sharp and restore more reliable object structures Table 3. Metrics of different network versions on NYU V2 Depth dataset. The “params” is the total number ...