license: apache-2.0
tags:
- gaussian-splatting
- 3d-reconstruction
- novel-view-synthesis
- feed-forward
datasets:
- DL3DV/DL3DV-10K
ReTokenGS — dens
Recurrent Gaussian Tokens with on-demand densification, built on LocusGS / TokenGS: the grad model (one weight-shared decoder layer × 12 rounds, per-token render-gradient feedback) plus a split step after every round driven by the same gradient — tokens whose Gaussians receive large positional gradients split into opacity-corrected children, so the token budget grows per scene from 1024 and keeps improving up to a 4096 cap that was never used in training. 4 posed input views, 256×256. No error cross-attention.
model.safetensors is the average of the last five training checkpoints (epochs 44–64).
| Model (official DL3DV benchmark: 140 scenes, 4 views, 256×256) | Tokens at R12 | PSNR | LPIPS | SSIM |
|---|---|---|---|---|
LocusGS stage 1, released base weights (same evaluation) |
1024 | 24.154 | 0.2766 | 0.7805 |
ReTokenGS-grad (Yusin/ReTokenGS-grad) |
1024 | 24.733 | 0.2664 | 0.7916 |
| ReTokenGS-dens, training threshold τ = 5.4e-5 | ≈ 1650 | 24.867 | 0.2611 | 0.7948 |
ReTokenGS-dens, DENS_TAU=3.4e-6 DENS_MAX_TOKENS=4096 |
4096 | 25.091 | 0.2442 | 0.8031 |
Code, evaluation and training commands: GitHub Yusin2Chen/TokenGS-Loop, branch dens. License: Apache-2.0 (derived from LocusGS / TokenGS).
The numbers were produced with the released code, which has no error cross-attention; the loop_err_* tensors in this file are unused and ignored at load time.