SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table J: The Depth Estimation Error (DEE) of 32 monocular depth estimation models in the KITTI-S benchmark. Blocks from top to bottom: [1st] the baseline MonoDepth2_R18 [17]; [2nd] methods w/ monocular inputs; [3rd] methods w/ stereo inputs; [4th] methods w/ monocular + stereo inputs. Bold: Best in col. Underline: Second best in col.
Method	mDEE	Cartoon	Digital Art	Ink Paint	Kids' Draw	Murals	Oil Paint	Penciling	Shadow Play	Sketch	Stained Glass	Relief	Water Color
MonoDepth2 R18 [17]	.365	.324	.434	.351	.259	.326	.328	.418	.388	.416	.566	.255	.317
MonoDepth2 nopt [17]	.378	.279	.289	.589	.235	.412	.249	.290	.589	.288	.596	.259	.464
MaskOcc R18 [48]	.368	.358	.356	.260	.265	.358	.375	.333	.314	.576	.554	.288	.384
DNet R18 [59]	.400	.444	.381	.293	.280	.389	.336	.422	.290	.608	.553	.290	.516
CADepth [60]	.406	.446	.380	.379	.250	.421	.413	.470	.317	.585	.543	.242	.401
HR-Depth [39]	.324	.318	.332	.238	.228	.299	.306	.337	.316	.388	.531	.265	.331
DIFFNet [67]	.310	.227	.351	.206	.206	.386	.244	.276	.289	.385	.502	.294	.360
ManyDepth [56]	.323	.251	.373	.212	.205	.344	.300	.360	.343	.331	.553	.310	.300
FSREDepth [22]	.293	.275	.277	.294	.221	.310	.220	.270	.301	.318	.417	.261	.352
MonoViT [66]	.238	.179	.229	.252	.196	.240	.203	.237	.208	.325	.356	.205	.221
DynaDepth R18 [64]	.371	.349	.358	.291	.264	.288	.293	.338	.363	.492	.534	.298	.585
DynaDepth R50 [64]	.447	.502	.408	.259	.287	.398	.437	.492	.372	.595	.565	.460	.589
RA-Depth [42]	.365	.304	.335	.354	.262	.340	.310	.342	.425	.372	.476	.379	.475
TriDepth R18 [6]	.379	.304	.403	.256	.217	.440	.352	.480	.318	.517	.573	.249	.436
Lite-Mono Tiny [63]	.280	.236	.310	.251	.221	.254	.283	.285	.232	.356	.446	.237	.252
Lite-Mono Small [63]	.303	.210	.346	.218	.210	.224	.238	.374	.307	.445	.435	.295	.336
Lite-Mono Base [63]	.288	.234	.313	.262	.218	.231	.241	.291	.240	.459	.476	.229	.264
Lite-Mono Large [63]	.323	.208	.435	.249	.306	.348	.258	.363	.210	.458	.507	.263	.266
MonoDepth2 R18 [17]	.456	.382	.483	.329	.299	.499	.442	.449	.388	.584	.770	.363	.486
MonoDepth2 nopt [17]	.427	.351	.334	.587	.234	.440	.309	.357	.475	.316	.774	.305	.643
MonoDepth2 R18 [17]	.414	.335	.444	.275	.267	.431	.402	.438	.367	.459	.809	.299	.438
MonoDepth2 nopt [17]	.403	.336	.350	.510	.236	.445	.338	.394	.457	.321	.743	.264	.433
CADepth [60]	.401	.398	.446	.265	.260	.476	.423	.479	.250	.615	.552	.314	.336
MonoViT [66]	.232	.173	.236	.224	.192	.208	.200	.226	.205	.315	.338	.217	.250
Figure J: Qualitative results of MonoDepth2 [17] under each corruption type across five severity levels. We show examples from the third level in the first column. The lighter regions correspond to near distances and vice versa. Best viewed in color. Zoomed-in for more details.
Figure K: Qualitative results of Lite-Mono [63] under each corruption type across five severity levels. We show examples from the third level in the first column. The lighter regions correspond to near distances and vice versa. Best viewed in color. Zoomed-in for more details.
Figure L: Qualitative results of MonoViT [66] under each corruption type across five severity levels. We show examples from the third level in the first column. The lighter regions correspond to near distances and vice versa. Best viewed in color. Zoomed-in for more details.
Figure M: Qualitative results of five monocular depth estimation models in the KITTI-C benchmark, including MonoDepth2 [17], DIFFNet [67], RA-Depth [42], Lite-Mono [63], and MonoViT [66]. The lighter regions correspond to near distances and vice versa. Best viewed in color. Zoomed-in for more details.
Figure N: Qualitative results of five monocular depth estimation models in the KITTI-C benchmark, including MonoDepth2 [17], DIFFNet [67], RA-Depth [42], Lite-Mono [63], and MonoViT [66]. The lighter regions correspond to near distances and vice versa. Best viewed in color. Zoomed-in for more details.
Figure O: Qualitative results of five monocular depth estimation models in the KITTI-S benchmark, including MonoDepth2 [17], DIFFNet [67], RA-Depth [42], Lite-Mono [63], and MonoViT [66]. The lighter regions correspond to near distances and vice versa. Best viewed in color. Zoomed-in for more details.
F Public Resources Used
In this section, we acknowledge the use of public resources, during the course of this work:
KITTI Vision Benchmark4 CC BY-NC-SA 3.0
- NYU Depth Dataset V2<sup>5</sup> Unknown
- nuScenes6 CC BY-NC-SA 4.0
- nuScenes-devkit<sup>7</sup> Apache License 2.0
- Cityscapes<sup>8</sup> Custom Cityscapes License
- Foggy-Cityscapes<sup>9</sup> Custom Foggy-Cityscapes License
-  ${\mathrm{{ACDC}}}^{10}$  . Custom ACDC License
- SeasonDepth Benchmark Toolkit<sup>11</sup> CC BY-NC-SA 4.0
- Make3D12 Custom Make3D License
- MonoDepth213 Custom MonoDepth2 License
- DepthHints14 Custom DepthHints License
- MaskOcc<sup>15</sup> Custom MonoDepth2 License
DNet<sup>16</sup> Custom MonoDepth2 License
- CADepth<sup>17</sup> MIT License
HR-Depth<sup>18</sup> MIT License
DIFFNet<sup>19</sup> Unknown
- ManyDepth20 Custom ManyDepth License
- FSRE-Depth21 MIT License
- MonoViT22 MIT License
DynaDepth23 Unknown
- RA-Depth24 Unknown
- TriDepth25 GNU General Public License v3.0
- Lite-Mono26 Unknown
4https://www.cvlabs.net/datasets/kitti.
$^{5}$ https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html.
$^{6}$ https://www.nuscenes.org/nuscenes.
<sup>7</sup>https://github.com/nutonomy/nuscenes-devkit.
$^{8}$ https://github.com/mcordts/cityscapesScripts.
<sup>9</sup>https://github.com/sakaridis/fog_simulation-SFSU_SYNTHETIC.
10 https://acdc.vision.ee.ethz.ch.
11https://github.com/SeasonDepth/SeasonDepth.
12http://make3d.cs.cornell.edu/data.html.
<sup>13</sup>https://github.com/nianticlabs/monodepth2.
$^{14}$ https://github.com/nianticlabs/depth-hints.
15https://github.com/schelv/monodepth2.
16https://github.com/TJ-IPLab/DNet.
17https://github.com/kamiLight/CADepth-master.
18https://github.com/shawLyu/HR-Depth.
19https://github.com/brandleyzhou/DIFFNet.
20https://github.com/nianticlabs/manydepth.
21https://github.com/hyBlue/FSRE-Depth.
22https://github.com/zxcqlf/MonoViT.
$^{23}$ https://github.com/SenZHANG-GitHub/ekf-imu-depth.
$^{24}$ https://github.com/hmhemu/RA-Depth.
25https://github.com/xingyuuchen/tri-depth.
$^{26}$ https://github.com/noahzn/Lite-Mono.
- Monocular-Depth-Estimation-Toolbox27 Apache License 2.0
- BTS28 GNU General Public License v3.0
- AdaBins29 ......... GNU General Public License v3.0
DPT30 MIT License
- SimIPU31 Apache License 2.0
- DepthFormer32 Apache License 2.0
- ImageCorruptions33 Apache License 2.0
- 3DCC $^{34}$  Attribution-NC 4.0 International
- ImageNet-C35 Apache License 2.0
- StylizeDatasets36 MIT License
- PyTorch-AdaIN<sup>37</sup> MIT License
27https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox.   
28https://github.com/cleinc/bts.   
29https://github.com/shariqfarooq123/AdaBins.   
30https://github.com/isl-org/DPT.   
31https://github.com/zhyever/SimIPU.   
32https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/tree/main/configs/   
depthformer.   
33https://github.com/bethgelab/imagecorruptions.   
34https://github.com/EPFL-VILAB/3DCommonCorruptions.   
35https://github.com/hendrycks/robustness.   
36https://github.com/bethgelab/stylize-datasets.   
37https://github.com/naoto0804/pytorch-AdaIN.