final.

README.md

@@ -1,4 +1,6 @@
 # Handcrafted Submission 2025-1
 
-This repo contains a submission to the [S23DR Challenge](https://huggingface.co/spaces/usm3d/S23DR) (part of the [USM3D](https://usm3d.github.io/) workshop at CVPR2025). It was prepared by [bulkobubulko](https://huggingface.co/bulkobubulko).
+This repo contains a submission to the [S23DR Challenge](https://huggingface.co/spaces/usm3d/S23DR2025) (part of the [USM3D](https://usm3d.github.io/) workshop at CVPR2025). It was prepared by [bulkobubulko](https://huggingface.co/bulkobubulko).
+
+
 

tools2025/hoho2025/example_solutions.py

@@ -27,6 +27,7 @@ def read_colmap_rec(colmap_data):
         rec = pycolmap.Reconstruction(tmpdir)
         return rec
 
+
 def convert_entry_to_human_readable(entry):
     out = {}
     for k, v in entry.items():
@@ -76,7 +77,6 @@ def point_to_segment_dist(pt, seg_p1, seg_p2):
     return np.linalg.norm(pt - proj)
 
 
-
 def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=25.0):
     """
     Identify apex and eave-end vertices, then detect lines for eave/ridge/rake/valley.
@@ -118,7 +118,6 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=25.0):
             vert = {"xy": centroids[i], "type": "eave_end_point"}
             vertices.append(vert)
 
-    # imrpoves 
     flashing_end_color = np.array(gestalt_color_mapping['flashing_end_point'])
     flashing_end_mask = cv2.inRange(gest_seg_np, flashing_end_color - 0.5, flashing_end_color + 0.5)
     if flashing_end_mask.sum() > 0:
@@ -145,12 +144,11 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=25.0):
                     'ridge', 
                     'rake', 
                     'valley', 
-                    ### improves 
+                    ###
                     'flashing',
                     'hip', 
                     'step_flashing',
                     'transition_line',
-                    ###
                     # 'cornice_return' # breaks
                     # 'cornice_strip' # breaks
                     ]
@@ -307,7 +305,6 @@ def get_uv_depth(vertices: List[dict],
     return uv, vertex_depth
 
 
-
 def project_vertices_to_3d(uv: np.ndarray, depth_vert: np.ndarray, col_img: pycolmap.Image) -> np.ndarray:
     """
     Projects 2D vertex coordinates with associated depths to 3D world coordinates.
@@ -564,6 +561,7 @@ def prune_not_connected(all_3d_vertices, connections_3d, keep_largest=True):
     new_conns = list(set([tuple(sorted(c)) for c in new_conns]))
     return new_vertices, new_conns
 
+
 def get_sparse_depth(colmap_rec, img_id_substring, depth):
     """
     Return a sparse depth map for the COLMAP image whose name contains
@@ -788,7 +786,7 @@ def predict_wireframe(entry) -> Tuple[np.ndarray, List[int]]:
         "p2d_uv_search_radius": 15,
         # 3D Merging & Pruning
         "p3d_merge_th": 0.4, # default=0.5, 0.4*, tighten the 3D merge radius
-        "p3d_prune_far_th": 1.0, # default=4.0, 3.0*
+        "p3d_prune_far_th": 3.0, # default=4.0, 3.0*
         "p3d_keep_largest_component": False,
         # Edge Validation
         "edge_val_p1_num_samples": 3,
@@ -810,7 +808,7 @@ def predict_wireframe(entry) -> Tuple[np.ndarray, List[int]]:
 
     for gest, depth, K, R, t, img_id, ade_seg in zip(
         good_entry['gestalt'], good_entry['depth'], good_entry['K'], good_entry['R'], 
-        good_entry['t'], good_entry['image_ids'], good_entry['ade']
+        good_entry['t'], good_entry['image_ids'], good_entry['ad e']
     ):
         depth_size = (np.array(depth).shape[1], np.array(depth).shape[0])
         gest_seg = gest.resize(depth_size)