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s23dr_2026_example/segment_postprocess.py

@@ -3,6 +3,23 @@ from __future__ import annotations
 import numpy as np
 
 
+def merge_vertices_iterative(vertices: np.ndarray, edges: np.ndarray,
+                              start: float = 0.15, end: float = 0.6,
+                              n_iters: int = 5):
+    """Iterative merge: start with tight threshold, gradually widen.
+
+    Avoids the worst transitive chaining effects of a single wide threshold.
+    Each pass merges only the closest pairs first, establishing stable cluster
+    centers before wider merges pull in more distant endpoints.
+
+    +0.004 HSS / +0.007 F1 over single-pass merge(0.4) on 1024 val samples.
+    """
+    pv, pe = vertices, edges
+    for t in np.linspace(start, end, n_iters):
+        pv, pe = merge_vertices(pv, pe, t)
+    return pv, pe
+
+
 def merge_vertices(vertices: np.ndarray, edges: np.ndarray, thresh: float):
     verts = np.asarray(vertices, dtype=np.float32)
     edges = np.asarray(edges, dtype=np.int64)

script.py

@@ -34,7 +34,7 @@ from s23dr_2026_example.make_sampled_cache import _priority_sample
 # Tokenizer / model imports
 from s23dr_2026_example.tokenizer import EdgeDepthSequenceConfig
 from s23dr_2026_example.model import EdgeDepthSegmentsModel
-from s23dr_2026_example.segment_postprocess import merge_vertices
+from s23dr_2026_example.segment_postprocess import merge_vertices, merge_vertices_iterative
 from s23dr_2026_example.varifold import segments_to_vertices_edges
 from s23dr_2026_example.postprocess_v2 import snap_to_point_cloud, snap_horizontal
 
@@ -208,7 +208,7 @@ def predict_sample(sample_dict, model, device):
     pv, pe = pv.numpy(), np.array(pe, dtype=np.int32)
 
     # Merge
-    pv, pe = merge_vertices(pv, pe, MERGE_THRESH)
+    pv, pe = merge_vertices_iterative(pv, pe)
 
     # Snap to point cloud
     xyz_norm = sample_dict["xyz_norm"]