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Semantic Occupancy Prediction on Occ3D (val)

49.4mIoUGaussianOcc3D
4.36816.05927.7539.441Nov 22, 2024Feb 2, 2025Apr 15, 2025Jun 27, 2025Sep 7, 2025Nov 18, 2025Jan 30, 2026

Evaluation results

MethodLinks
GaussianOcc3DModality=C&L2026.0149.412.554.234.556.459.732.443.748.632.740.849.581.140.354.160.969.769.5
GaussianOcc3DModality=Camera & LiDAR2026.0149.412.554.234.556.459.732.443.748.632.740.849.581.140.354.160.969.769.5
OccFusionModality=C&L2026.0148.712.451.83354.657.7344348.435.541.248.68344.757.16062.561.3
OccFusionModality=Camera & LiDAR2026.0148.712.451.83354.657.7344348.435.541.248.68344.757.16062.561.3
GaussianFormer3DModality=C&L2026.0146.49.85031.35459.428.136.246.226.740.249.779.137.3495569.167.6
GaussianFormer3DModality=Camera & LiDAR2026.0146.49.85031.35459.428.136.246.226.740.249.779.137.3495569.167.6
COTRModality=C2026.0144.513.352.1324655.632.632.830.434.137.741.884.546.257.660.75246.3
COTRModality=Camera2026.0144.513.352.1324655.632.632.830.434.137.741.884.546.257.660.75246.3
PanoOccModality=C2026.0142.111.750.529.649.455.523.333.330.63134.442.683.344.254.45645.940.4
FB-OccModality=C2026.0142.114.349.73046.651.529.329.129.430.53539.483.147.255.659.944.939.6
PanoOccModality=Camera2026.0142.111.750.529.649.455.523.333.330.63134.442.683.344.254.45645.940.4
FB-OccModality=Camera2026.0142.114.349.73046.651.529.329.129.430.53539.483.147.255.659.944.939.6
BEVDet-Occ+VisionPADBackbone=R50, Image Size=384 x 704, Pre-train by Det.=true, Temporal Frames=8f2024.1142—————————————————
BEVFormerVenue=ECCV'22, Backbone=R101, Image Size=900 x 1600, Pre-train by Det.=true2024.1139.3—————————————————
BEVDet-OccVenue=ArXiv'22, Backbone=R50, Image Size=384 x 704, Pre-train by Det.=true, Temporal Frames=8f2024.1139.3—————————————————
BEVDet4DModality=C2026.0139.39.347.119.241.552.227.221.223.321.635.838.982.540.453.857.749.945.8
BEVDet4DModality=Camera2026.0139.39.347.119.241.552.227.221.223.321.635.838.982.540.453.857.749.945.8
FB-OccVenue=CVPRW'23, Backbone=R50, Image Size=384 x 704, Pre-train by Det.=true, Temporal Frames=16f2024.1139.1—————————————————
OPUSVenue=NeurIPS'24, Backbone=R50, Image Size=256 x 704, Pre-train by Det.=false, Temporal Frames=8f2024.1136.2—————————————————
GaussianFormerModality=C2026.0135.58.840.923.342.949.719.224.824.422.529.435.37936.946.648.238.833.1
GaussianFormerModality=Camera2026.0135.58.840.923.342.949.719.224.824.422.529.435.37936.946.648.238.833.1
UVTR+VisionPADBackbone=ConvNeXt-S, Image Size=900 x 1600, Pre-train by Det.=false2024.1135.4—————————————————
TPVFormerVenue=CVPR'23, Backbone=R50, Image Size=900 x 1600, Pre-train by Det.=true2024.1134.2—————————————————
UVTR+UniPADVenue=CVPR'24, Backbone=ConvNeXt-S, Image Size=900 x 1600, Pre-train by Det.=false2024.1131—————————————————
SparseOccVenue=ECCV'24, Backbone=R50, Image Size=256 x 704, Pre-train by Det.=false, Temporal Frames=16f2024.1130.6—————————————————
UVTR+Venue=NeurIPS'22, Backbone=ConvNeXt-S, Image Size=900 x 1600, Pre-train by Det.=false2024.1130.1—————————————————
TPVFormerModality=C2026.0128.36.739.214.241.54719.222.617.914.530.235.556.233.735.731.62016.1
TPVFormerModality=Camera2026.0128.36.739.214.241.54719.222.617.914.530.235.556.233.735.731.62016.1
RenderOccModality=C2026.0126.14.831.710.727.726.513.918.217.717.821.223.363.236.446.244.319.620.7
RenderOccModality=Camera2026.0126.14.831.710.727.726.513.918.217.717.821.223.363.236.446.244.319.620.7
RenderOccVenue=ICRA'24, Backbone=Swin-B, Image Size=512 x 1408, Pre-train by Det.=true2024.1124.5—————————————————
BEVFormerModality=C2026.0123.7538.81034.441.113.216.518.217.818.727.74927.729.125.415.414.5
BEVFormerModality=Camera2026.0123.7538.81034.441.113.216.518.217.818.727.74927.729.125.415.414.5
BEVDetModality=C2026.0111.72.115.304.2131.400.40.136.66.752.71926.521.814.515.3
BEVDetModality=Camera2026.0111.72.115.304.2131.400.40.136.66.752.71926.521.814.515.3
MonoSceneModality=C2026.016.11.87.24.34.99.45.7435.94.57.214.96.37.97.417.7
MonoSceneModality=Camera2026.016.11.87.24.34.99.45.7435.94.57.214.96.37.97.417.7