File size: 7,404 Bytes
5b6e3d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | """Junction-type constraints for 3D roof wireframes.
After merging per-view detections into a 3D graph, we apply simple topology
priors to drop obviously wrong edges/vertices:
1. Collinear merge: if a vertex has degree 2 with two nearly antiparallel edges,
it is most likely a spurious point on a longer edge — merge the edges and
drop the vertex.
2. Duplicate-direction prune: if a vertex has two incident edges that point in
(nearly) the same direction, keep only the stronger one (stronger = higher
sklearn score if available, else longer edge).
3. Isolated leaf prune: vertices with degree 1 whose only edge is very short
(< 0.4 m) are dropped — they are almost always noise.
The module is intentionally pure-numpy and side-effect-free so it can be
dropped into both the heuristic and the triangulation pipelines.
"""
from __future__ import annotations
import numpy as np
from typing import Sequence
def _edge_directions(vertices: np.ndarray, edges: np.ndarray) -> np.ndarray:
"""Unit vectors for each edge (from a→b). Shape (E, 3)."""
if len(edges) == 0:
return np.empty((0, 3), dtype=np.float32)
diffs = vertices[edges[:, 1]] - vertices[edges[:, 0]]
norms = np.linalg.norm(diffs, axis=1, keepdims=True)
norms = np.where(norms < 1e-6, 1.0, norms)
return diffs / norms
def _build_adj(n_vertices: int, edges: np.ndarray):
"""Return list[list[(neighbour, edge_index)]]."""
adj = [[] for _ in range(n_vertices)]
for ei, (a, b) in enumerate(edges):
adj[int(a)].append((int(b), ei))
adj[int(b)].append((int(a), ei))
return adj
def apply_junction_constraints(
vertices: np.ndarray,
edges: Sequence[tuple],
edge_scores: np.ndarray | None = None,
collinear_cos: float = 0.97,
duplicate_cos: float = 0.985,
leaf_min_len: float = 0.4,
max_passes: int = 3,
) -> tuple[np.ndarray, list]:
"""Apply junction-type constraints to a 3D wireframe.
Parameters
----------
vertices : (N, 3) array of 3D vertex positions.
edges : list of (i, j) undirected edges.
edge_scores : optional (E,) array in [0, 1] giving edge confidence.
When missing, all edges are treated as equal (tie-break by length).
collinear_cos : cosine threshold above which two incident edges are
considered antiparallel → triggers collinear merge.
duplicate_cos : cosine threshold above which two incident edges pointing
the same way are treated as duplicates → keep only the stronger one.
leaf_min_len : edges shorter than this feeding a degree-1 vertex get cut.
max_passes : how many passes to iterate since removing one edge can
create new opportunities.
Returns
-------
(vertices_new, edges_new) where vertices_new may keep indices identical
to the input (we do not reindex; instead we return only the surviving
subset of edges). Fully-isolated vertices are filtered by callers that
already run `prune_not_connected`.
"""
verts = np.asarray(vertices, dtype=np.float32)
edges_arr = np.asarray(list(edges), dtype=np.int64) if len(edges) else np.empty((0, 2), dtype=np.int64)
if len(edges_arr) == 0 or len(verts) == 0:
return verts, list(edges)
if edge_scores is None:
scores = np.ones(len(edges_arr), dtype=np.float32)
else:
scores = np.asarray(edge_scores, dtype=np.float32)
if len(scores) != len(edges_arr):
scores = np.ones(len(edges_arr), dtype=np.float32)
alive = np.ones(len(edges_arr), dtype=bool)
for _ in range(max_passes):
changed = False
directions = _edge_directions(verts, edges_arr)
lengths = np.linalg.norm(
verts[edges_arr[:, 1]] - verts[edges_arr[:, 0]], axis=1
)
adj = _build_adj(len(verts), edges_arr[alive])
# We need the original edge indices, not the compacted ones, for mutation.
# Rebuild adjacency using absolute indices.
adj = [[] for _ in range(len(verts))]
for ei, (a, b) in enumerate(edges_arr):
if not alive[ei]:
continue
adj[int(a)].append((int(b), ei))
adj[int(b)].append((int(a), ei))
# Pass 1: collinear merge on degree-2 vertices
for v in range(len(verts)):
if len(adj[v]) != 2:
continue
(n1, e1), (n2, e2) = adj[v]
if n1 == n2:
continue
# Direction from v outward
d1 = verts[n1] - verts[v]
d2 = verts[n2] - verts[v]
l1, l2 = np.linalg.norm(d1), np.linalg.norm(d2)
if l1 < 1e-6 or l2 < 1e-6:
continue
d1 /= l1
d2 /= l2
# Antiparallel = straight line through v
if float(np.dot(d1, d2)) < -collinear_cos:
# Merge: kill e1, reroute e2 to connect (n1, n2)
if (n1, n2) in {tuple(edges_arr[i]) for i in range(len(edges_arr)) if alive[i]} or \
(n2, n1) in {tuple(edges_arr[i]) for i in range(len(edges_arr)) if alive[i]}:
# Already exists — just drop both incident edges (degenerate)
alive[e1] = False
alive[e2] = False
else:
alive[e1] = False
edges_arr[e2] = (min(n1, n2), max(n1, n2))
changed = True
break
if changed:
continue
# Pass 2: duplicate-direction prune
for v in range(len(verts)):
if len(adj[v]) < 2:
continue
nbrs = adj[v]
# Build direction vectors for each incident alive edge
dirs = []
for nb, ei in nbrs:
d = verts[nb] - verts[v]
nrm = np.linalg.norm(d)
if nrm < 1e-6:
dirs.append(None)
else:
dirs.append(d / nrm)
# Find any duplicate pair
drop_ei = None
for i in range(len(nbrs)):
if dirs[i] is None:
continue
for j in range(i + 1, len(nbrs)):
if dirs[j] is None:
continue
if float(np.dot(dirs[i], dirs[j])) > duplicate_cos:
ei_i, ei_j = nbrs[i][1], nbrs[j][1]
# Keep the one with higher score; tiebreak by length
s_i = (scores[ei_i], lengths[ei_i])
s_j = (scores[ei_j], lengths[ei_j])
drop_ei = ei_j if s_i >= s_j else ei_i
break
if drop_ei is not None:
break
if drop_ei is not None:
alive[drop_ei] = False
changed = True
break
if changed:
continue
# Pass 3: leaf prune (degree-1 short edge)
for v in range(len(verts)):
if len(adj[v]) != 1:
continue
nb, ei = adj[v][0]
if lengths[ei] < leaf_min_len:
alive[ei] = False
changed = True
break
if not changed:
break
surviving = [tuple(map(int, edges_arr[i])) for i in range(len(edges_arr)) if alive[i]]
return verts, surviving
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