image_to_mesh() now runs the real cascade, not the single-stage shortcut:
structure 3048 voxels @32^3 (64^3 occupancy, MAX-POOLED DOWN)
LR SLAT 3048 x 32 shape_512 extractor
refine 13147 coords @64^3 four decoder stages -> coords -> quantise
HR SLAT 13147 x 32 shape_1024 extractor
mesh 3988052 verts, 7996876 faces @1024^3
TOTAL 258.4s, peak 27.9GB with every model resident silhouette IoU 0.969
Three things the cascade needed:
1. occupied_coords_at() - ss_dec always decodes 64^3 but the cascade STARTS at 32^3.
Upstream max-pools the boolean grid down by the ratio (a voxel survives if ANY of
its eight children was occupied). I had been feeding the raw 64^3 set to the HR flow.
2. decoder.upsample() - pushes the LR latent four stages in and returns COORDS, not
features. The predicted subdivisions grow the occupied set; those coords quantise
onto the HR flow's grid. Stops BEFORE stage `upsample_times`, as upstream does;
one stage further doubles the resolution and misplaces every voxel.
3. grid_resolution override on ProjConditioner - upstream backs the HR grid off in
128-unit steps while the token count exceeds max_num_tokens, so a dense object
degrades instead of exploding. refine_coords() implements that loop.
I WAS WRONG ABOUT THE HALO. The previous commit blamed the single-stage shortcut for a
0.639 silhouette IoU and predicted the cascade would fix it. The cascade measured
0.640 - no change. The real fault was in my VERIFICATION, not the pipeline: o_voxel
returns vertices in the voxel-grid frame, while ProjGrid rotates its lattice by
_BLENDER_ROT before projecting. Rotating the mesh the same way scores 0.969 on the
same geometry the earlier commit had already produced. Added mesh.to_camera_frame()
so the trap is named where it bites; the earlier mesh was correct all along.
The cascade is still the right thing - it is the shipped path, and staged loading
halves peak memory (12.8GB vs 22.6GB) when models are released between stages.
Also adds models.load_all(), so a server builds all five models plus both conditioners
ONCE. Warmup is ~71s against ~17s of compute, so an operator must never fork per job.
Holding everything resident costs 27.9GB peak - nothing on a 256GB box.
scripts/image_to_mesh.py exits non-zero if IoU < 0.85: a run that completes with a bad
reconstruction has failed even though nothing raised.
27/27 green.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
All seven Pixal3D models now load and run. shape_dec 292/292, tex_dec 284/284, both
with zero missing/unmapped/mismatched keys - the 8-param difference between them is
exactly the four to_subdiv layers, since tex_dec has pred_subdiv=False.
Behaviour is right: 12 latent voxels grow SELECTIVELY through four stages
(12 -> 91 -> 193 -> 358 -> 1150) rather than x8 each time, which would have reached
49,152. Scale lands at 1/16. shape_dec emits the hardcoded 7 channels and its vertex
head produces offsets inside the [-0.5, 1.5] band its sigmoid+voxel_margin allows.
tex_dec, guided by shape_dec's masks, reproduces exactly the same voxel count.
VERIFICATION CAVEAT, recorded in the README: these two are the only models using sparse
conv, so upstream cannot run here and there is no numerical oracle. Unlike the five
models verified at correlation 1.0, these are checked structurally and behaviourally
only. Weaker evidence, and labelled as such rather than presented alongside the
verified results.
