GEM represents driving scenes as explicit continuous 4D Gaussian primitives with learned dynamics to enable direct querying at arbitrary timestamps for semantic occupancy forecasting and motion planning.
SparseBEV: High- performance sparse 3d object detection from multi-camera videos
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GEM: Gaussian Evolution Model for Occupancy Forecasting and Motion Planning
GEM represents driving scenes as explicit continuous 4D Gaussian primitives with learned dynamics to enable direct querying at arbitrary timestamps for semantic occupancy forecasting and motion planning.