CARLA-GS is a modular pipeline that uses an LLM for semantic trajectory planning, CARLA for physics execution, and 3D Gaussian Splatting for photorealistic rendering to synthesize autonomous driving corner cases.
Drivinggaussian: Composite gaussian splatting for surrounding dy- namic autonomous driving scenes
3 Pith papers cite this work. Polarity classification is still indexing.
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Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.
MesonGS++ achieves over 34x compression of 3D Gaussian Splatting models post-training while preserving or exceeding original rendering quality through size-aware hyperparameter optimization.
citing papers explorer
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CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis
CARLA-GS is a modular pipeline that uses an LLM for semantic trajectory planning, CARLA for physics execution, and 3D Gaussian Splatting for photorealistic rendering to synthesize autonomous driving corner cases.
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Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes
Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.
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MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
MesonGS++ achieves over 34x compression of 3D Gaussian Splatting models post-training while preserving or exceeding original rendering quality through size-aware hyperparameter optimization.