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.
Street Gaussians: Modeling dynamic urban scenes with gaussian splatting
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.