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Kuan Wei Huang commited on
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1 Parent(s): f1e6a32

renamed visual -> visuals and updated README

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  1. README.md +11 -10
  2. visual/Aachen_Cathedral.tar.gz +3 -0
  3. visual/Abbatiale_d'Ottmarsheim.tar.gz +3 -0
  4. visual/Abbaye_Saint-Jouin_de_Marnes.tar.gz +3 -0
  5. visual/Abbaye_de_Chaalis_-_Abbatiale.tar.gz +3 -0
  6. visual/Abbaye_de_Morienval.tar.gz +3 -0
  7. visual/Abbaye_de_Moyenmoutier.tar.gz +3 -0
  8. visual/Abbaye_de_Saint-Amand-de-Coly.tar.gz +3 -0
  9. visual/Abbaye_de_Saint-Pierre_d'Airvault.tar.gz +3 -0
  10. visual/Abbazia_di_Pomposa.tar.gz +3 -0
  11. visual/Abbey_Church,_Murbach.tar.gz +3 -0
  12. visual/Abtei_Sayn.tar.gz +3 -0
  13. visual/Abteikirche_Marienstatt.tar.gz +3 -0
  14. visual/Akdamar_Church.tar.gz +3 -0
  15. visual/Alcázar_de_Jerez_de_la_Frontera.tar.gz +3 -0
  16. visual/All_Saints,_Brixworth.tar.gz +3 -0
  17. visual/All_saints_church_(Stvolínky).tar.gz +3 -0
  18. visual/Alt_St._Alban_(Köln).tar.gz +3 -0
  19. visual/Alte_Nikolaikirche_(Frankfurt_am_Main).tar.gz +3 -0
  20. visual/Altenberger_Dom.tar.gz +3 -0
  21. visual/Amin_al-Din_Jebrail_Mausoleum.tar.gz +3 -0
  22. visual/Amphitheatre,_Trier.tar.gz +3 -0
  23. visual/Amphitheatre_(Pompeii).tar.gz +3 -0
  24. visual/Ancient_Greek-Roman_theatre_(Taormina).tar.gz +3 -0
  25. visual/Angkor_Wat.tar.gz +3 -0
  26. visual/Arena_(Verona).tar.gz +3 -0
  27. visual/Ateni_Sioni_Church.tar.gz +3 -0
  28. visual/Auditorium_Theatre_(Chicago).tar.gz +3 -0
  29. visual/Bangor_Cathedral.tar.gz +3 -0
  30. visual/Baptistry_(Padua).tar.gz +3 -0
  31. visual/Bartholomäuskirche_Markgröningen.tar.gz +3 -0
  32. visual/Basilica_(Pompeii).tar.gz +3 -0
  33. visual/Basilica_St._Castor_(Koblenz).tar.gz +3 -0
  34. visual/Basilica_dei_Santi_Apostoli_(Rome).tar.gz +3 -0
  35. visual/Basilica_of_Saint_Praxedes_(Rome).tar.gz +3 -0
  36. visual/Basilica_of_San_Domenico_(Siena).tar.gz +3 -0
  37. visual/Basilika_Seckau.tar.gz +3 -0
  38. visual/Basilique_Notre-Dame-des-Victoires.tar.gz +3 -0
  39. visual/Basilique_Notre-Dame_de_Bonne-Espérance.tar.gz +3 -0
  40. visual/Basilique_Saint-Denis.tar.gz +3 -0
  41. visual/Basilique_Saint-Eutrope_de_Saintes.tar.gz +3 -0
  42. visual/Basilique_Saint-Martin_d'Ainay_(Lyon).tar.gz +3 -0
  43. visual/Basilique_Saint-Nazaire_de_Carcassonne.tar.gz +3 -0
  44. visual/Basilique_Saint-Remi_de_Reims.tar.gz +3 -0
  45. visual/Basilique_Saint-Sauveur_de_Dinan.tar.gz +3 -0
  46. visual/Basilique_du_Sacré-Cœur_de_Montmartre.tar.gz +3 -0
  47. visual/Basilique_du_Sacré-Cœur_de_Paray-le-Monial.tar.gz +3 -0
  48. visual/Basílica_de_la_Asunción_de_Nuestra_Señora_de_Lequeitio.tar.gz +3 -0
  49. visual/Bayerisches_Nationalmuseum.tar.gz +3 -0
  50. visual/Berlin_Cathedral.tar.gz +3 -0
README.md CHANGED
@@ -8,7 +8,7 @@ license: cc-by-4.0
8
  **C3** contains **91K paired floor plans and photos from the Internet** across **574 scenes** with **155M pixel-level correspondences** and **86K camera poses**.
9
 
10
  ## Correspondences and Camera Poses
11
- `data/` is split into `train/` and `test/`, each with the same structure: `image_pairs.csv`, `correspondences/`, and `camera_poses/`.
12
 
13
  - `image_pairs.csv`: Each row represents a plan-photo pair, consisting of `id`, `scene_name`, `floor_plan_path`, and `photo_path`. `id` is used to reference correspondeing files in `correspondences/` and `camera_poses`, named using the format `{id:05d}.npy`.
14
 
@@ -17,7 +17,7 @@ license: cc-by-4.0
17
  - `camera_poses/`: Each `.npy` file contains a list of [camera_rotation (3,3), camera_position (3,), camera_intrinsics (3,3)]. Files are grouped in batches of 1,000.
18
 
19
  ```
20
- data/
21
  ├── train/
22
  │ ├── image_pairs.csv
23
  │ ├── correspondences/
@@ -40,9 +40,9 @@ data/
40
  └── (same structure as train)
41
  ```
42
  ## Floor Plans and Photos
43
- `visuals/` contains floor plans and photos grouped by scenes.
44
  ```
45
- visuals/
46
  ├── Aachen_Cathedral.tar.gz
47
  ├── Abbatiale_d'Ottmarsheim.tar.gz
48
  └── ...
@@ -76,17 +76,18 @@ import pandas as pd
76
  from PIL import Image
77
 
78
  # load example data
79
- data_train = "data/train/
80
- image_pairs = pd.read_csv(join(data_train, "image_pairs.csv"), header=None)
81
  id, scene_name, recon_num, floor_plan_path, image_path = image_pairs.iloc[0]
82
 
83
- plan = Image.open(join("visuals", scene_name, floor_plan_path))
84
- photo = Image.open(join("visuals", scene_name, image_path))
85
- corr_path = join(data_train, "correspondences", "0", f"{id:06d}.npy")
86
  plan_corr, photo_corr = np.load(corr_path)
87
 
 
 
 
88
 
89
- # plotting
90
  fig, axes = plt.subplots(1, 2, figsize=(12, 6))
91
  fig.suptitle(f"Scene: {scene_name}", fontsize=16)
92
 
 
8
  **C3** contains **91K paired floor plans and photos from the Internet** across **574 scenes** with **155M pixel-level correspondences** and **86K camera poses**.
9
 
10
  ## Correspondences and Camera Poses
11
+ `geometric/` is split into `train/` and `test/`, each with the same structure: `image_pairs.csv`, `correspondences/`, and `camera_poses/`.
12
 
13
  - `image_pairs.csv`: Each row represents a plan-photo pair, consisting of `id`, `scene_name`, `floor_plan_path`, and `photo_path`. `id` is used to reference correspondeing files in `correspondences/` and `camera_poses`, named using the format `{id:05d}.npy`.
14
 
 
17
  - `camera_poses/`: Each `.npy` file contains a list of [camera_rotation (3,3), camera_position (3,), camera_intrinsics (3,3)]. Files are grouped in batches of 1,000.
18
 
19
  ```
20
+ geometric/
21
  ├── train/
22
  │ ├── image_pairs.csv
23
  │ ├── correspondences/
 
40
  └── (same structure as train)
41
  ```
42
  ## Floor Plans and Photos
43
+ `visual/` contains floor plans and photos grouped by scenes.
44
  ```
45
+ visual/
46
  ├── Aachen_Cathedral.tar.gz
47
  ├── Abbatiale_d'Ottmarsheim.tar.gz
48
  └── ...
 
76
  from PIL import Image
77
 
78
  # load example data
79
+ geometric_train_dir = "geometric/train/
80
+ image_pairs = pd.read_csv(join(geometric_train_dir, "image_pairs.csv"), header=None)
81
  id, scene_name, recon_num, floor_plan_path, image_path = image_pairs.iloc[0]
82
 
83
+ corr_path = join(geometric_train_dir, "correspondences", "0", f"{id:06d}.npy")
 
 
84
  plan_corr, photo_corr = np.load(corr_path)
85
 
86
+ visual_dir = "visual"
87
+ plan = Image.open(join(visual_dir, scene_name, floor_plan_path))
88
+ photo = Image.open(join(visual_dir, scene_name, image_path))
89
 
90
+ # visualize correspondences
91
  fig, axes = plt.subplots(1, 2, figsize=(12, 6))
92
  fig.suptitle(f"Scene: {scene_name}", fontsize=16)
93
 
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