QtMeshEditor — Mesh Part Segmentation

Model

• Input: a sampled point cloud float32 [1, N, 3] (normalised to a centred unit box; +Y up, character facing +Z, left limbs at +X).
• Output: per-point class logits over 7 labels (unknown, head, torso, left_arm, right_arm, left_leg, right_leg); argmax → label, scattered back to mesh vertices/faces by nearest sampled point.
• Architecture (v2): 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).
• When the model is unavailable, QtMeshEditor falls back to a deterministic geometric segmenter (connected-component islands + an up-axis/lateral spatial heuristic, refined by skeleton-bone proximity when the mesh is rigged).

Training data & license

1. Synthetic surface-sampled bodies (CC0, ours): three parametric body plans — humanoid (normal / chibi / lanky proportion regimes, ears/muzzle head bumps, posed arms, feet pointing +Z), quadruped (all four legs labelled by side, tail → torso), and biped-with-tail (dino) — with exact by-construction labels and randomised per-part point density (real characters put 30–50% of their vertices in the head/face).
2. CC0 rigged characters (Quaternius packs — humanoids, farm animals, dinosaurs, monsters) mined for exact rig-derived labels (bone weights → bone name → part) via qtmesh segment --dump-training-data. Each mined cloud is canonicalised into the training frame from its own labels and its arm/leg sides are reassigned geometrically. A provenance ledger of every pack is kept with the training corpus.

Evaluation (v2)

Reproducing

Versions

• v2.0.0 (current) — full data-pipeline rebuild fixing v1's real-mesh failures: surface sampling instead of volumetric primitive fills, connected bodies, chibi/cartoon proportions (v1 collapsed big-headed characters to all-torso), randomised per-part vertex density, correct left/right handedness (v1's training data was mirrored vs the rig convention), quadruped + biped-tail body plans, mined CC0 rigs with label-driven canonicalisation, a deeper two-block kNN network, and fine-tuning at the app's 4096-point inference size (v1 trained at 1024). 31.5% → 94.7% on rig-truth eval. ~0.78 MB.
• v1.1.0 — retrained with T/A-pose arms, upright + yaw-only augmentation, head-protrusion samples, wider proportions, and a PointNet++-style local kNN feature. Clean on synthetic humanoids but poor transfer to real meshes (volumetric sampling, flipped handedness). ~0.58 MB.
• v1.0.0 — initial flat-PointNet model on synthetic primitive humanoids (arms-down only; over-assigned lateral protrusions to "arm").