--- 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_plan-to-cam (3, 3), t_plan (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() ``` ![Example Visualization](example.png) ## 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} } ```