Yusin/ReTokenGS-dens

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.