minor updates

README.md

@@ -3,7 +3,7 @@ license: cc-by-4.0
 ---
 # C3: Cross-View Cross-Modality Correspondence Dataset
 ## Dataset for *C3Po: Cross-View Cross-Modality Correspondence with Pointmap Prediction*
-[Paper (coming soon)](https://c3po-correspondence.github.io/) | [Project Website](https://c3po-correspondence.github.io/) | [GitHub](https://github.com/c3po-correspondence/C3Po)
+[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**.
 
@@ -12,11 +12,11 @@ license: cc-by-4.0
 - `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}/caemra_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>cam-to-world</sub> (3, 3), t<sub>world</sub> (3,), K (3, 3)] 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/
@@ -76,17 +76,17 @@ import numpy as np
 import pandas as pd
 from PIL import Image
 
-def draw_camera_frustum(ax, t_w, R_c2w, frustum_length, frustum_width, color='blue', alpha=0.3):
+def draw_camera_frustum(ax, t_p, R_p2c, frustum_length, frustum_width, color='blue', alpha=0.3):
     # Camera axes
-    forward = R_c2w[:, 2]  
-    right = R_c2w[:, 0]    
+    forward = R_p2c[:, 2]  
+    right = R_p2c[:, 0]    
 
     # 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_c2w @ t_w
+    cc = -R_p2c.T @ t_p
     points = np.array([
         cc + forward * near_len - right * near_width / 2,  # near left
         cc + forward * near_len + right * near_width / 2,  # near right
@@ -111,9 +111,9 @@ 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_c2w, t_w, _ = np.load(camera_pose_path, allow_pickle=True)
-R_c2w = np.array(R_c2w.tolist(), dtype=float)
-t_w = np.array(t_w)
+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/"
@@ -128,7 +128,7 @@ 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_w, R_c2w, frustum_length=scale, frustum_width=scale, color='blue', alpha=0.3)
+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)

geometric/geometric_test.tar.gz

@@ -1,3 +1,3 @@
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+size 243057920

geometric/geometric_train.tar.gz

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geometric/geometric_val.tar.gz

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