2ede6655d9
3 Commits
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m3ultra
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516e3e4457 |
Mesh cleanup: weld, strip floaters, iterative decimation
The raw cascade output is ~4M verts / 8M faces and is not usable as-is. cleanup.py is ordinary mesh hygiene, kept out of the model code, and the ORDER is the whole point: weld -> strip floaters -> decimate -> strip again -> fix normals CORRECTION TO THE FLOATER COUNT. The health pass reported 52,855 components with 52,838 fragments under 100 faces, and I took those for stray shells. They were mostly NOT: the decoder emits per-voxel vertices, so coincident corners are duplicated and the same continuous surface reads as tens of thousands of islands. Welding FIRST collapses it to a single component, and only 6,332 faces are genuinely stray. Ordering the pass the other way round removes 250k faces of real geometry and calls it cleaning. Two performance fixes, both because an operator runs this every job: - Component labelling is a scipy union-find over the VERTEX graph, not trimesh.face_adjacency. Same answer, ~240s -> ~1s on this mesh. - fix_normals runs LAST, on the decimated mesh. It walks face adjacency, so on the raw 7.99M-face mesh it costs minutes and the result is then thrown away by decimation. Cleanup went ~243s -> ~20s. Decimation is iterative. A single fast_simplification call will not reduce past roughly 4.4% of its input whatever target_reduction (or agg) is asked for: from 7.99M faces, targets of 200k, 50k and 20k ALL returned 351,535. Repeated smaller passes get further because each re-evaluates quadrics on the collapsed mesh. MEASURED, on the sample: target 500,000 -> 499,984 faces silhouette IoU 0.965 19.3s target 200,000 -> 214,322 faces silhouette IoU 0.823 34.8s target 100,000 -> 214,322 faces silhouette IoU 0.823 target 20,000 -> 214,322 faces silhouette IoU 0.823 HONEST LIMITATION: ~214k is a hard floor, and reaching it costs real fidelity (0.965 -> 0.823). The cause is the ~180,000 BOUNDARY edges the Flexible Dual Grid produces for open surfaces - quadric decimation will not collapse those, and no aggressiveness setting changes it. Below ~214k needs a REMESH, not a decimator. 500k is effectively lossless and is the setting to use; anything under 214k is not currently reachable and the loop stops rather than spinning. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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m3ultra
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165de26db5 |
The shipped cascade: image -> GLB at silhouette IoU 0.969
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> |
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m3ultra
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79ef81988c |
Real image -> occupancy grid, with a silhouette check and honest timings
image_to_occupancy() runs the structure stage on an actual photo: preprocess ->
DINOv3 -> proj back-projection -> ss_flow -> ss_dec -> 64^3 occupancy.
VERIFICATION THAT MATTERS: scripts/run_structure.py re-projects the occupied voxels
through the same camera and compares against the input alpha matte. On the upstream
sample that is silhouette IoU 0.842 with 12948 voxels occupied (4.94% of 64^3). This
is the model's own headline claim, so it is the right thing to assert — 'it ran
without crashing' would pass just as happily on a generic blob.
Two real bugs this phase found, neither visible without reading the shipped configs:
1. THE SAMPLER WAS MISSING guidance_rescale. The checkpoint's own pipeline.json sets
0.7 for the structure stage and 0.5 for shape_slat, so this fires at the model's
DEFAULT settings — omitting it silently overcooks every structure prediction. Now
implemented (Lin et al. CFG rescale) and diffed against upstream's
ClassifierFreeGuidanceSamplerMixin, run directly rather than reimplemented.
2. The sampler defaults were wrong: the real ss stage is steps=12 / rescale_t=5.0 /
guidance 7.5 / interval [0.6,1.0], not the steps=25 / rescale_t=3.0 the smoke test
assumed. All three stages' real params now live in pipeline.py, read from
pipeline.json rather than guessed.
TIMINGS, measured with interleaved reps after warmup (the first pass attributed the
same 11s of residual warmup to both 'rescale' and 'torch contention'; it was neither):
cold run 89.3s
warm, full settings 16.5s
warm, CFG off 9.2s -> CFG costs 1.80x, as expected for 10/12
steps falling inside the guidance interval
guidance_rescale ~0s -> free
torch/MPS contention ~0s -> DINOv3 can stay resident
peak memory 6.8GB
THE FINDING THAT SHAPES THE OPERATOR: warmup is ~71s against ~17s of actual compute,
i.e. 4x the work. A MODELBEAST operator MUST hold the models resident across jobs
rather than fork per job — the trellis2 lane shows the same shape (47.9s cold vs 2.5s
warm pipeline_load). Cost this in before optimising any kernel.
17/17 tests green (12 proj + 5 sampler).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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