| | --- |
| | license: cc-by-4.0 |
| | --- |
| | # C3: Cross-View Cross-Modality Correspondence Dataset |
| | ## Dataset for *C3Po: Cross-View Cross-Modality Correspondence with Pointmap Prediction* |
| | [arXiv](https://arxiv.org/abs/2511.18559) | [Project Website](https://c3po-correspondence.github.io/) | [GitHub](https://github.com/c3po-correspondence/C3Po) |
| |
|
| | **C3** contains **90K paired floor plans and photos from the Internet** across **597 scenes** with **153M pixel-level correspondences** and **85K camera poses**. |
| |
|
| | ## Image Pairs |
| | `image_pairs/` is split into `train/`, `val`, and `test/`, each with a `image_pairs.csv`. |
| | - `image_pairs.csv`: Each row represents a plan-photo pair, consisting of `uid`, `scene_name`, `plan_path`, and `photo_path`. `uid` is used to reference corresponding files in `correspondences/` and `camera_poses`, named using the format `{int(uid):06d}.npy`. `scene_name` is used to reference corresponding floor plans (`visual/{scene_name}/{plan_path}`) and photos (`visual/{scene_name}/{photo_path}`). |
| |
|
| | ## Correspondences and Camera Poses |
| | `geometric/` has three files: `geometric_train.tar.gz`, `geometric_val.tar.gz`, and `geometric_test.tar.gz`. Each of these files (`geometric_{split}.tar.gz`) can be extracted to `{split}/correspondences/` and `{split}/camera_poses/`. |
| |
|
| | - `correspondences/`: Each `.npy` files contains an array of [plan_correspondences (M, 2), photo_correspondences (M, 2)] and are grouped in batches of 1,000. |
| |
|
| | - `camera_poses/`: Each `.npy` file contains an array of [R<sub>plan-to-cam</sub> (3, 3), t<sub>plan</sub> (3,), K (3, 3)] and are grouped in batches of 1,000. |
| |
|
| | ``` |
| | geometric/ |
| | ├── train/ # Extracted from geometric_train.tar.gz |
| | │ ├── correspondences/ |
| | │ │ ├── 0/ |
| | │ │ │ ├── 00000.npy |
| | │ │ │ ├── ... |
| | │ │ │ ├── 00999.npy |
| | │ │ ├── ... |
| | │ ├── camera_poses/ |
| | │ │ ├── 0/ |
| | │ │ │ ├── 00000.npy |
| | │ │ │ ├── ... |
| | │ │ │ ├── 00999.npy |
| | │ │ ├── ... |
| | ├── val/ # Extracted from geometric_val.tar.gz |
| | │ ├── (same structure as train) |
| | └── test/ # Extracted from geometric_test.tar.gz |
| | └── (same structure as train) |
| | |
| | ``` |
| | ## Floor Plans and Photos |
| | `visual/` contains floor plans and photos grouped by scenes. |
| | ``` |
| | visual/ |
| | ├── Aachen_Cathedral.tar.gz |
| | ├── Abbatiale_d'Ottmarsheim.tar.gz |
| | └── ... |
| | |
| | ``` |
| | ### Archived Contents |
| | Each `{scene_name}.tar.gz` file contains the following structure when extracted: |
| | ``` |
| | ├── images/ |
| | │ ├── commons/ # arbitrary number of subcategories |
| | │ │ ├── {wikimedia_commons_subcategory_1}/ # arbitrary number of photos |
| | │ │ │ ├── {photo_A}.png |
| | │ │ │ ├── ... |
| | │ │ ├── {wikimedia_commons_subcategory_2}/ |
| | │ │ │ ├── ... |
| | │ │ ├── ... |
| | │ ├── flickr/ # arbitrary number of photos |
| | │ │ ├── {photo_B}.png |
| | │ │ ├── ... |
| | ├── plans/ # arbitrary number of floor plans |
| | ├── {floor_plan_A}.png |
| | ├── ... |
| | ``` |
| |
|
| | ## Example Visualization |
| | ```python |
| | from os.path import join |
| | |
| | import matplotlib.pyplot as plt |
| | import numpy as np |
| | import pandas as pd |
| | from PIL import Image |
| | |
| | def draw_camera_frustum(ax, t_p, R_p2c, frustum_length, frustum_width, color='blue', alpha=0.3): |
| | # Camera axes |
| | forward = R_p2c.T[:, 2] |
| | forward_xz = forward.copy() |
| | forward_xz[1] = 0 # Project onto the XZ plane |
| | |
| | if np.linalg.norm(forward_xz) < 1e-6: |
| | forward_xz = np.array([0, 0, 1]) |
| | else: |
| | forward_xz /= np.linalg.norm(forward_xz) |
| | |
| | right_xz = np.cross(np.array([0, 1, 0]), forward_xz) |
| | right_xz /= np.linalg.norm(right_xz) |
| | |
| | # Near and far plane distances |
| | near_len, far_len = frustum_length * 0.2, frustum_length |
| | near_width, far_width = frustum_width * 0.2, frustum_width |
| | |
| | # Corner points of the frustum |
| | cc = -R_p2c.T @ t_p |
| | points = np.array([ |
| | cc + forward_xz * near_len - right_xz * near_width / 2, # near left |
| | cc + forward_xz * near_len + right_xz * near_width / 2, # near right |
| | cc + forward_xz * far_len + right_xz * far_width / 2, # far right |
| | cc + forward_xz * far_len - right_xz * far_width / 2. # far left |
| | ]) |
| | |
| | x, z = points[:, 0], points[:, 2] |
| | ax.fill(x, z, color=color, alpha=alpha) |
| | ax.plot(np.append(x, x[0]), np.append(z, z[0]), color=color) |
| | |
| | |
| | # Load image pair |
| | image_pairs_path = "image_pairs/train/image_pairs.csv" |
| | image_pairs = pd.read_csv(image_pairs_path) |
| | uid, scene_name, plan_path, photo_path = image_pairs.iloc[0] |
| | |
| | # Load correspondences |
| | geometric_train_dir = "geometric/train/" |
| | corr_path = join(geometric_train_dir, "correspondences", f"{int(uid) // 1000}", f"{int(uid):06d}.npy") |
| | plan_corr, photo_corr = np.load(corr_path) |
| | |
| | # Load camera pose |
| | camera_pose_path = join(geometric_train_dir, "camera_poses", f"{int(uid) // 1000}", f"{int(uid):06d}.npy") |
| | R_p2c, t_p, _ = np.load(camera_pose_path, allow_pickle=True) |
| | R_p2c = np.array(R_p2c.tolist(), dtype=float) |
| | t_p = np.array(t_p) |
| | |
| | # Load floor plan and photo |
| | visual_dir = "visual/" |
| | plan = Image.open(join(visual_dir, scene_name, plan_path)).convert("RGB") |
| | photo = Image.open(join(visual_dir, scene_name, photo_path)).convert("RGB") |
| | |
| | # Visualize |
| | fig, axes = plt.subplots(1, 2, figsize=(12, 6)) |
| | fig.suptitle(f"Scene name: {scene_name}", fontsize=16) |
| | |
| | axes[0].imshow(plan) |
| | axes[0].set_title("Floor Plan") |
| | axes[0].scatter(plan_corr[:, 0], plan_corr[:, 1], c="r", s=1) |
| | scale = max(plan.size) * 0.05 |
| | draw_camera_frustum(axes[0], t_p, R_p2c, frustum_length=scale, frustum_width=scale, color='blue', alpha=0.3) |
| | axes[0].axis('off') |
| | |
| | axes[1].imshow(photo) |
| | axes[1].set_title("Photo") |
| | axes[1].scatter(photo_corr[:, 0], photo_corr[:, 1], c="r", s=1) |
| | axes[1].axis('off') |
| | |
| | plt.tight_layout() |
| | plt.show() |
| | ``` |
| |  |
| |
|
| | ## Citation |
| | If you use data from C3, please cite with the following: |
| | ``` |
| | @inproceedings{ |
| | huang2025c3po, |
| | title={C3Po: Cross-View Cross-Modality Correspondence by Pointmap Prediction}, |
| | author={Huang, Kuan Wei and Li, Brandon and Hariharan, Bharath and Snavely, Noah}, |
| | booktitle={Advances in Neural Information Processing Systems}, |
| | volume={38}, |
| | year={2025} |
| | } |
| | ``` |
