Show the numbers
| Training step | Before the fix | After the fix |
|---|---|---|
| 1,000 | 11.11 | 14.27 |
| 2,000 | 10.54 | 14.36 |
| 3,000 | 10.64 | 14.75 |
| 4,000 | 9.67 | 14.66 |
| 5,000 | 9.56 | 14.84 |
| 6,000 | 9.75 | 14.79 |
| 7,000 | 10.33 | 14.84 |
| 8,000 | 9.20 | 14.76 |
| 9,000 | 9.48 | 15.22 |
| 10,000 | 9.40 | 15.14 |
| 11,000 | 9.50 | 15.07 |
| 12,000 | 9.56 | 15.14 |
| 13,000 | 9.44 | 15.00 |
| 14,000 | 9.73 | 14.98 |
| 15,000 | 9.78 | 15.14 |
| 16,000 | 9.90 | 15.05 |
| 17,000 | 9.37 | 15.12 |
| 18,000 | 9.46 | 15.10 |
| 19,000 | 9.65 | 15.16 |
| 20,000 | 9.79 | 15.19 |
A model that turns a few phone photos of any car into a 3D splat in one pass. Our first training run got worse for 19,000 straight steps. The cause was a PyTorch function silently returning wrong gradients for the memory layout our renderer produces. After a one-line fix, the same model trains properly.
What we measured
| Measure | Before the fix | After the fix | Note |
|---|---|---|---|
| Best score on 5 unseen cars | 11.1 dB | 15.22 dB | AnySplat: 14.6 |
| Score at the end of training | 9.8 dB | 15.19 dB | |
| One fixed image pair, 600 steps | 14.7 → 9.5 dB | 14.7 → 30.2 dB | should always improve |
| Gradient check on the SSIM term | 0.026 | 1.001 | 1.00 means the gradient is right |
| Training time, one RTX 4070 | n/a | 30 min |
What happens next
- Let the input photos exchange information before placing points, which is what the strongest 360° methods do.
- Re-test SSIM now that its gradient is fixed, and train on more cars.
The book behind the lab
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