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RLGS_models

Reinforcement Learning
gaussian-splatting
3d-gaussian-splatting
novel-view-synthesis
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metadata 
license: mit
tags:
  - gaussian-splatting
  - 3d-gaussian-splatting
  - reinforcement-learning
  - novel-view-synthesis

RLGS models

Pretrained 3D Gaussian Splatting models and per-scene evaluation results for RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting.

Paper: arXiv:2508.04078

Repository layout 

rlgs_tmgs/
  tandt/<scene>/        # Tanks and Temples
  db/<scene>/           # Deep Blending
  dl3dv/<scene>/        # DL3DV
rlgs_mcmc/
  mipnerf360/<scene>/   # Mip-NeRF 360

Each scene folder contains:

File Contents
point_cloud/iteration_30000/point_cloud.ply Trained Gaussians at iteration 30,000
results.json PSNR, SSIM and LPIPS

Download 

hf download stack93/RLGS_models --local-dir RLGS_models --include "rlgs_tmgs/tandt/truck/*"

Remove --include to download everything (about 44 GB).

Results

Metrics from each scene's results.json, at iteration 30,000.

Dataset Folder Scenes PSNR (dB) SSIM LPIPS
Tanks and Temples rlgs_tmgs/tandt 2 24.74 0.866 0.158
Deep Blending rlgs_tmgs/db 2 30.26 0.911 0.233
DL3DV rlgs_tmgs/dl3dv 140 29.96 0.912 0.125
Mip-NeRF 360 rlgs_mcmc/mipnerf360 7 30.12 0.897 0.144

Tanks and Temples

Scene PSNR (dB) SSIM LPIPS
train 23.24 0.837 0.191
truck 26.25 0.895 0.124

Deep Blending

Scene PSNR (dB) SSIM LPIPS
drjohnson 29.88 0.910 0.232
playroom 30.65 0.913 0.234

DL3DV

Show all 140 scenes
Scene PSNR (dB) SSIM LPIPS
032dee 31.94 0.937 0.087
0569e8 29.96 0.929 0.059
06da79 29.19 0.913 0.138
073f5a 23.96 0.737 0.266
07d9f9 38.85 0.984 0.023
085397 35.49 0.963 0.076
093ef3 31.91 0.956 0.097
0a1b7c 33.87 0.967 0.077
0a4853 33.25 0.956 0.076
0bfdd0 25.94 0.808 0.208
119fd5 27.47 0.866 0.218
126493 24.25 0.760 0.218
14eb48 29.28 0.898 0.185
15ff83 27.89 0.871 0.132
165f5a 25.65 0.813 0.178
183dd2 31.34 0.939 0.126
1ba74c 32.94 0.934 0.169
1d6a9e 23.23 0.780 0.183
1da888 33.85 0.958 0.095
1de58b 37.46 0.980 0.037
238554 29.46 0.932 0.083
26fd23 30.38 0.908 0.114
280abf 32.10 0.953 0.085
286239 26.01 0.866 0.126
2991a7 26.30 0.854 0.164
2b65ba 28.30 0.913 0.120
2beaca 32.86 0.956 0.066
2cbfe2 35.65 0.974 0.045
2f3e1c 28.21 0.888 0.149
32c2b9 32.75 0.927 0.205
341b4f 28.93 0.943 0.096
35317e 27.92 0.887 0.117
358723 33.51 0.964 0.051
374ffd 33.58 0.952 0.070
387eeb 32.95 0.953 0.081
389a46 32.35 0.941 0.186
3b16a1 26.19 0.860 0.155
3b7529 32.44 0.950 0.086
3bb3bb 30.50 0.946 0.111
3bb894 28.44 0.904 0.105
410367 32.89 0.958 0.078
444da1 28.05 0.932 0.116
457e9a 29.02 0.919 0.188
484c0a 27.91 0.920 0.154
493816 28.96 0.928 0.065
4ae797 30.28 0.940 0.088
4ff865 34.09 0.965 0.063
50c46c 32.56 0.947 0.135
513e4e 30.57 0.932 0.116
54bf35 28.84 0.905 0.117
56452d 25.19 0.792 0.230
565553 29.76 0.929 0.084
599ca3 27.42 0.895 0.160
5a27c0 31.55 0.960 0.063
5a69d1 26.01 0.844 0.133
5c3af5 31.73 0.952 0.053
5c8daf 31.52 0.955 0.098
5f0041 33.94 0.969 0.046
63798f 32.20 0.941 0.187
669c36 28.80 0.933 0.111
66fd66 33.53 0.965 0.082
6d2216 27.62 0.862 0.225
6d81c5 24.80 0.829 0.230
6e11e7 34.06 0.959 0.165
70eac6 27.53 0.922 0.104
71b2dc 35.46 0.974 0.063
75fbbe 30.82 0.939 0.099
7705a2 31.48 0.908 0.189
7a9f97 31.00 0.926 0.116
7da3db 29.14 0.917 0.148
800cf8 27.47 0.870 0.146
8324b3 25.88 0.887 0.139
85cd0e 29.11 0.906 0.145
8b9fb9 24.44 0.825 0.172
8cb2e9 31.67 0.924 0.155
8fdc51 29.46 0.913 0.125
90cb7e 30.15 0.938 0.091
917e9c 32.14 0.955 0.077
918c8d 27.61 0.892 0.138
91afb9 32.66 0.956 0.076
946f49 33.05 0.952 0.065
9641a1 26.71 0.856 0.237
9c8c0e 25.97 0.821 0.155
9cbc55 30.85 0.945 0.078
9e9a89 31.97 0.950 0.156
9fb058 28.37 0.928 0.087
a17a98 31.20 0.952 0.094
a40146 28.09 0.898 0.122
a62c33 27.15 0.894 0.123
a62f9a 32.26 0.955 0.087
a726c1 26.24 0.899 0.191
adb95f 27.01 0.842 0.181
adf351 30.42 0.906 0.204
af0d70 27.37 0.906 0.124
b2076b 30.02 0.944 0.104
b3bf90 30.38 0.923 0.099
b4f530 31.42 0.951 0.063
b5faa2 28.65 0.852 0.238
b6d113 30.75 0.951 0.060
b92b49 33.79 0.962 0.106
ba55c8 35.61 0.964 0.073
c07692 38.05 0.980 0.052
c37109 22.17 0.686 0.292
c37726 25.54 0.839 0.193
c45589 27.41 0.890 0.135
cbd44b 34.19 0.962 0.083
cc08c0 28.47 0.900 0.141
cd9c98 28.76 0.924 0.095
ceb252 33.90 0.958 0.084
d1b3a0 25.80 0.867 0.135
d3812a 21.68 0.756 0.316
d3af82 32.58 0.942 0.091
d4fbeb 26.72 0.893 0.094
d8de66 29.42 0.897 0.144
d904ae 25.91 0.835 0.171
d9b637 24.59 0.792 0.202
d9f4c7 34.92 0.975 0.047
dac979 31.13 0.949 0.116
dafa9c 23.86 0.811 0.170
ddfdcf 29.41 0.938 0.080
ded5e4 32.58 0.930 0.166
df04f5 33.11 0.960 0.042
df29c2 35.71 0.964 0.061
df4f9d 33.27 0.934 0.197
e5684b 29.58 0.906 0.171
e78f8c 30.91 0.945 0.054
e8ce51 27.98 0.932 0.124
e9360e 26.19 0.885 0.146
eb4cf5 31.26 0.937 0.148
ec1e44 29.71 0.938 0.123
ec3057 29.22 0.926 0.092
ed1632 29.21 0.891 0.118
ef59aa 29.83 0.866 0.139
f004c8 35.13 0.976 0.055
f477ff 31.14 0.946 0.153
f71ac3 30.04 0.936 0.053
f7aaea 28.44 0.929 0.086
fb2c04 28.12 0.909 0.110
fb3b73 24.34 0.773 0.210
ff5923 33.09 0.948 0.108

Mip-NeRF 360

Scene PSNR (dB) SSIM LPIPS
bicycle 26.30 0.810 0.163
bonsai 33.26 0.955 0.160
counter 29.70 0.927 0.159
garden 28.36 0.887 0.087
kitchen 32.64 0.941 0.105
room 32.99 0.940 0.168
stump 27.60 0.818 0.166

License

This repository is released under the MIT License.

Citation 

@article{li2025rlgs,
  title={RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting},
  author={Li, Zhan and Zhan, Huangying and Li, Changyang and Yan, Qingan and Xu, Yi},
  journal={arXiv preprint arXiv:2508.04078},
  year={2025}
}