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Novel View Synthesis on Tanks&Temples

91.5SSIMTeacher-Hash
60.09268.24676.484.554Nov 29, 2022May 6, 2023Oct 12, 2023Mar 18, 2024Aug 24, 2024Jan 29, 2025Jul 7, 2025

Evaluation results

MethodLinks
Teacher-HashArchitecture=Hash Grid, Role=Teacher2022.1191.529.26——————————0.106————0.134
TensoRF-VMArchitecture=VM-decomposition, Training=From scratch2022.1190.928.06——————————0.155————0.145
s-VMArchitecture=VM-decomposition, Method=PVD (Distillation)2022.1189.927.86——————————0.181————0.176
s-MLPArchitecture=MLP, Method=PVD (Distillation)2022.1189.127.5——————————0.19————0.194
s-TensorsArchitecture=Sparse Tensors, Method=PVD (Distillation)2022.1186.625.31——————————0.22————0.263
PlenoxelsArchitecture=Sparse Tensors, Training=From scratch2022.1186.525.18——————————0.261————0.219
NeRFArchitecture=MLP, Training=From scratch2022.1186.425.78————————————————
Textured GaussiansPercentage of Gaussians=100%2024.1185.4224.260.1684———————————————
3DGS*Percentage of Gaussians=100%2024.1185.4124.180.1754———————————————
3D-GSModel Compression=1.00x, Rendering Speedup=1.00x, Training Speedup=1.00x2024.1184.923.70.178———————————————
SnugBoxModel Compression=0.99x, Rendering Speedup=1.61x, Training Speedup=1.11x2024.1184.923.690.178———————————————
AccuTileModel Compression=0.99x, Rendering Speedup=1.67x, Training Speedup=1.12x2024.1184.923.730.177———————————————
Baseline 3DGSTraining Iterations=300002024.1284.723.620.1761.841——————————————
MaskGaussianTraining=30000 iterations + 5000 finetune iterations2024.1284.723.750.1780.579——————————————
LightGaussianTraining=30000 iterations + 5000 finetune iterations2024.1284.323.750.1870.626——————————————
Soft PruningModel Compression=1.69x, Rendering Speedup=2.48x, Training Speedup=1.36x2024.1184.123.540.201———————————————
ReducingModel Compression=2.56x, Rendering Speedup=1.91x, Training Speedup=1.27x2024.118423.570.188———————————————
Mini-SplatModel Compression=9.20x2024.1183.523.180.202———————————————
3D-GS2025.0383.124.36——————————0.213—————
TrimmingModel Compression=4.00x2024.1183.123.690.21———————————————
CompactModel Compression=2.19x, Rendering Speedup=1.16x, Training Speedup=0.76x2024.1183.123.320.201———————————————
Textured GaussiansPercentage of Gaussians=10%2024.1182.5923.410.2122———————————————
ELMGSModel Compression=5.00x, Rendering Speedup=4.05x2024.1182.523.90.233———————————————
FA-NeRF2025.0382.124.45——————————0.205—————
Hard PruningModel Compression=10.1x, Rendering Speedup=6.30x, Training Speedup=1.58x2024.1182.123.450.241———————————————
EAGLESRendering Speedup=1.73x, Training Speedup=1.19x2024.118223.10.22———————————————
LightModel Compression=2.94x, Rendering Speedup=1.97x2024.1181.723.110.231———————————————
3DGS*Percentage of Gaussians=10%2024.1180.222.820.2728———————————————
PUPModel Compression=10.0x, Rendering Speedup=4.00x2024.1180.122.720.244———————————————
Mip-NeRF3602025.0375.922.22——————————0.257—————
Mip-NeRF 3602024.1175.922.220.257———————————————
iNGP-BigModel size=Big2025.0375.721.69——————————0.28—————
Textured GaussiansPercentage of Gaussians=1%2024.1173.9921.10.3104———————————————
iNGP-BaseModel size=Base2025.0373.121.56——————————0.318—————
Instant-NGP2024.1172.321.720.33———————————————
DRAE2025.0767.5—————————————————
3DGS*Percentage of Gaussians=1%2024.1167.3619.90.4406———————————————
GNT-MOVE2025.0764—————————————————
TensoRF2025.0361.319.52——————————0.451—————
DRAESetting=Generalizable, Zero-shot=true2025.07————19.7162.816.2749.913.5649.519.150.1——————
EVSInput View Number=62021.03————17.7458.815.3843.416.458.521.5770.2—0.3010.4340.3140.243—
FVSInput View Number=62021.03————15.8263.813.7150.211.4954.814.9558.6—0.3180.4470.4860.417—
FVSInput View Number=Whole2021.03————22.6286.717.975.817.1478.522.0383.7—0.110.220.290.16—
GNTSetting=Generalizable, Zero-shot=true2025.07————17.3956.114.094211.2941.915.3641.7——————
NeRFInput View Number=Whole2021.03————20.8574.716.6463.516.8670.221.5576.5—0.5130.6510.6020.529—
SVNVSInput View Number=62021.03————21.3370.818.8154.219.273.222.1271—0.2330.3860.250.245—
SVNVSInput View Number=22021.03————19.262.717.4446.418.1466.719.9860.1—0.2940.4750.3030.35—
SVNVSInput View Number=42021.03————20.4168.217.8249.519.3769.821.3166.3—0.2540.430.2830.275—