The whole geometry chain now runs on a real photograph:
[1] occupancy 12948 voxels 19.7s
[2] cond proj (1, 262144, 2048) @ 64^3 2.6s
gathered proj (12948, 2048)
[3] SLAT (12948, 32) 88.7s
[4] MESH 3556515 verts, 7071196 faces 11.0s grid 1024^3
peak 22.6 GB, bounds inside the unit cube
Two bugs fixed on the way:
1. "global" must be FLAT [M,C] for the sparse blocks, not the dense stage's [B,T,C].
The sparse cross-attention takes a token stack plus an explicit layout, so the
dense shape dies inside to_kv's reshape rather than anywhere informative. Gathering
now reshapes it, and refuses batch > 1 rather than silently mislabelling a layout.
2. o_voxel needs the decoder OUTPUT grid, not its configured resolution. The shape
decoder applies four 2x upsamples, so a res-64 latent decodes into 1024^3, while
the config says 256 (upstream overrides it per run via set_resolution). Passing 256
raised an opaque out-of-bounds inside o_voxel's hashmap insert. Added
output_resolution() and a guard that names the real cause.
HONEST LIMITATION - this is NOT yet the shipped cascade. Upstream's
sample_shape_slat_cascade runs the 512 flow (res 32) first, denormalises, UPSAMPLES
THE COORDINATE SET through the shape decoder, then runs the 1024 flow on the refined
coords. Running the HR flow straight off the 64^3 occupancy set yields a complete,
exportable mesh whose silhouette IoU is 0.639 - against 0.842 for the occupancy grid
that seeded it. The gap is a halo of geometry outside the true silhouette, exactly
what the missing coordinate refinement would prune. Do not read the current mesh
quality as the model's; wiring the cascade is the next step.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Every SparseConv3d in the Pixal3D model is (channels, out, 3) - stride=1/padding=None,
so spconv dispatches to SubMConv3d, which trellis_sparse_mlx already implements and
benchmarks. attn_mode is 'full' throughout. The container split (VarLenTensor /
SparseTensor) and the get/register_spatial_cache spelling are already in the shared core.
Remaining gap is two ops: SparseUpsample and SparseSpatial2Channel, both cache-paired
with a matching downsample rather than recomputing structure.
