QtMeshEditor — Building Part Segmentation
A point-cloud part-segmentation network (PointNet++-style) that labels each
point of a building mesh as wall, roof, window, door, chimney,
or foundation, exported to ONNX for local inference via ONNX Runtime.
One of the category-specialised segmentation models built for
QtMeshEditor (epic #818,
Track B2) — a free, open-source 3D mesh & animation editor. The app
auto-detects the mesh category with a companion
point-cloud classifier
and dispatches to this model for buildings; siblings:
body,
vegetation,
vehicle.
Aggregate download source used by the app:
QtMeshEditor-models
(segment/meshseg_building.onnx).
Model
- Input: a sampled point cloud
float32 [1, N, 3](normalised to a centred unit box; +Y up). - Output: per-point class logits over 7 channels
(
unknown, wall, roof, window, door, chimney, foundation); argmax → label, scattered back to mesh vertices/faces by nearest sampled point. - Architecture: shared per-point MLP + two kNN local-aggregation blocks
(in-graph
cdist+topk, ONNX-exportable) + a global max-pooled feature; ~0.78 MB. Trained at the app's inference sample size (4096 points).
Training data & license
Trained from scratch, 100% on procedurally generated synthetic buildings we own (no third-party data at all): parametric houses / towers / huts with gable, pyramid, and flat roofs (triangle/quad surface patches), proud window panes in rows, doors, chimneys, and foundation slabs — labels are exact by construction. Weights released under CC-BY-4.0; please credit QtMeshEditor.
Evaluation
- Held-out synthetic validation accuracy: 86.9% (per-point, unknown masked). Real-world CC0 building kits are the planned next data slice (mined via submesh/material-name labels).
Reproducing
scripts/export-meshseg-onnx.py --category building in the QtMeshEditor repo
(one-time, offline; the app never runs Python). Strategy + roadmap:
docs/MESH_SEGMENTATION_STRATEGY.md.
Versions
- v1.0.0 (current) — initial synthetic-only release (#818 Track B2).