RecGen — sparse-structure denoiser, finetuned (70k)
An updated sparse-structure (SS) denoiser for RecGen. It is a drop-in
replacement for the stereo_denoiser_ema0.9999_step0055000.pt at the repository
root; the SLAT denoiser is unchanged and should still be taken from the root.
stereo_denoiser_ema0.9999_step0070000.pt this checkpoint
stereo_config.json required — architecture + pose representation
stereo_pose_stats.json required — pose normalization statistics
Usage
Pair it with the SLAT denoiser at the repository root:
from recgen_utils import load_pipeline
pipeline = load_pipeline(
checkpoint_slat="slat_denoiser_ema0.9999_step0075000.pt", # repo root, unchanged
checkpoint_sparse="sparse-structure-ft-70k/stereo_denoiser_ema0.9999_step0070000.pt",
)
What changed
Finetuned from the released SS checkpoint on a preprocessed multi-view dataset with SAM2-mixed masks and per-sample color jitter.
| initialized from | released stereo_denoiser_ema0.9999_step0055000.pt |
| optimizer | AdamW, lr 1e-5, weight decay 0 |
| augmentation | color jitter (brightness/contrast/saturation 0.4, hue 0.1) |
| masks | mixed source, 50% SAM2 |
| EMA | 0.9999 |
| architecture | unchanged — 1024 channels, 2 pose tokens, 6d_translation_scale, 2 views @ 518px |
Evaluation
Full HomebrewedDB evaluation, 539 samples, single-view conditioning, with the SLAT denoiser held fixed at the released checkpoint so that differences are attributable to the sparse-structure stage alone.
| metric | released SS @55k | this checkpoint | |
|---|---|---|---|
| ADD-S recall @2% of diameter | 0.048 | 0.093 | ~1.9× |
| Chamfer distance, median (cm) | 0.410 | 0.403 | |
| Chamfer distance, 90th pct (cm) | 0.800 | 0.740 | −7.5% |
| Chamfer distance, pre-ICP median (cm) | 0.669 | 0.627 | −6.3% |
ADD-S recall at the conventional 10%-of-diameter threshold is saturated for both checkpoints (0.950 vs 0.961) and does not separate them; the tighter 2% threshold is where the difference appears. The improvement is concentrated in pose accuracy and in the hard tail rather than in median reconstruction accuracy.
License
CC-BY-NC-4.0, as the rest of this repository.