The shape decoder's 7 channels are not an SDF — O-Voxel solves a QEF over a Flexible
Dual Grid, which is what lets it carry open and non-manifold surfaces:
0:3 vertex offset in-voxel, (1+2m)*sigmoid(v)-m so it may sit OUTSIDE its own cell
3:6 per-axis intersection logits, thresholded at 0
6:7 quad split weight through softplus
mesh.py is the MLX->torch boundary for export. o_voxel's convert/postprocess are
native (C++/Metal) and deliberately NOT ported: o-voxel builds a CPU CppExtension when
CUDA is absent, and the trellis-2 lane on this fleet already runs it with a Metal
baker, so reusing that build beats reimplementing a QEF solver in MLX. Installed into
the shared venv from ~/Documents/trellis-2-mrp-mlx/o-voxel; it needs cv2 and xatlas,
and NOT utils3d (which drags in open3d, with no cp312 wheel).
Verified against the REAL shape_dec (292/292 params, resolution 256):
decoded 5954 voxels x 7ch -> 5954 vertices, 6886 faces -> GLB written
Not watertight, correctly: the input was a random latent, and FlexiDualGrid represents
open surfaces by design. Vertices land inside the octant of the unit cube matching the
sparse coords fed in, which is the check that the grid indexing is right.
Still to wire: the SLAT stage itself (sparse latents seeded from the occupancy coords,
with proj features gathered at those coords).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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