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  1. ibims/readme.txt +12 -0
  2. nyuv2/readme.txt +5 -0
  3. scannet/readme.txt +5 -0
  4. sintel/alley_1/frame_0001_img.png +3 -0
  5. sintel/alley_1/frame_0001_normal.npy +3 -0
  6. sintel/alley_1/frame_0002_img.png +3 -0
  7. sintel/alley_1/frame_0002_normal.npy +3 -0
  8. sintel/alley_1/frame_0003_img.png +3 -0
  9. sintel/alley_1/frame_0003_normal.npy +3 -0
  10. sintel/alley_1/frame_0004_img.png +3 -0
  11. sintel/alley_1/frame_0004_normal.npy +3 -0
  12. sintel/alley_1/frame_0005_img.png +3 -0
  13. sintel/alley_1/frame_0005_normal.npy +3 -0
  14. sintel/alley_1/frame_0006_img.png +3 -0
  15. sintel/alley_1/frame_0006_normal.npy +3 -0
  16. sintel/alley_1/frame_0007_img.png +3 -0
  17. sintel/alley_1/frame_0007_normal.npy +3 -0
  18. sintel/alley_1/frame_0008_img.png +3 -0
  19. sintel/alley_1/frame_0008_normal.npy +3 -0
  20. sintel/alley_1/frame_0009_img.png +3 -0
  21. sintel/alley_1/frame_0009_normal.npy +3 -0
  22. sintel/alley_1/frame_0010_img.png +3 -0
  23. sintel/alley_1/frame_0011_img.png +3 -0
  24. sintel/alley_1/frame_0012_img.png +3 -0
  25. sintel/alley_1/frame_0012_normal.npy +3 -0
  26. sintel/alley_1/frame_0013_img.png +3 -0
  27. sintel/alley_1/frame_0013_normal.npy +3 -0
  28. sintel/alley_1/frame_0014_img.png +3 -0
  29. sintel/alley_1/frame_0015_img.png +3 -0
  30. sintel/alley_1/frame_0015_normal.npy +3 -0
  31. sintel/alley_1/frame_0016_img.png +3 -0
  32. sintel/alley_1/frame_0016_normal.npy +3 -0
  33. sintel/alley_1/frame_0017_img.png +3 -0
  34. sintel/alley_1/frame_0017_normal.npy +3 -0
  35. sintel/alley_1/frame_0018_img.png +3 -0
  36. sintel/alley_1/frame_0019_img.png +3 -0
  37. sintel/alley_1/frame_0019_normal.npy +3 -0
  38. sintel/alley_1/frame_0020_img.png +3 -0
  39. sintel/alley_1/frame_0020_normal.npy +3 -0
  40. sintel/alley_1/frame_0021_img.png +3 -0
  41. sintel/alley_1/frame_0021_normal.npy +3 -0
  42. sintel/alley_1/frame_0022_img.png +3 -0
  43. sintel/alley_1/frame_0022_normal.npy +3 -0
  44. sintel/alley_1/frame_0023_img.png +3 -0
  45. sintel/alley_1/frame_0023_normal.npy +3 -0
  46. sintel/alley_1/frame_0024_img.png +3 -0
  47. sintel/alley_1/frame_0024_normal.npy +3 -0
  48. sintel/alley_1/frame_0025_img.png +3 -0
  49. sintel/alley_1/frame_0026_img.png +3 -0
  50. sintel/readme.txt +5 -0
ibims/readme.txt ADDED
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+ iBims-1 dataset (https://www.asg.ed.tum.de/lmf/ibims1/)
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+ check the relevant LICENSE before using this dataset
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+
4
+ Provided by courtesy of Gwanbin Bae (https://github.com/baegwangbin/DSINE)
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+ @inproceedings{bae2024dsine,
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+ title = {Rethinking Inductive Biases for Surface Normal Estimation},
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+ author = {Gwangbin Bae and Andrew J. Davison},
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+ booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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+ year = {2024}
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+ }
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+
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+ GT surface normals are computed by doing discontinuity-aware plane fitting
nyuv2/readme.txt ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ NYUv2 dataset (https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)
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+ check the relevant LICENSE before using this dataset
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+
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+ GT surface normals are from GeoNet (CVPR 2018) - https://github.com/xjqi/GeoNet
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+
scannet/readme.txt ADDED
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+ ScanNet dataset (https://github.com/ScanNet/ScanNet)
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+ check the relevant LICENSE before using this dataset
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+
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+ GT surface normals and data split are from FrameNet (ICCV 2019) - https://github.com/hjwdzh/FrameNet
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+
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+ Sintel dataset (http://sintel.is.tue.mpg.de/)
2
+ check the relevant LICENSE before using this dataset
3
+
4
+ We computed the GT surface normals by doing discontinuity-aware plane fitting
5
+