Introduces the ASTAD task and training-free ASTModel framework for semantically consistent asymmetric style transfer using labeled synthetic content and unlabeled real references.
arXiv preprint arXiv:2510.10203 (2025)
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A real multi-city, multi-kilometer surround-view driving dataset plus an urban-tailored 3DGS baseline shows that city-scale reconstruction still degrades with scale, off-trajectory views, and real-world noise.
Introduces a path-based credibility framework using equivalent rainfall intensity, RRD scores from real raindrop spectra, and lidar consistency metrics to identify preferable simulated rainfall paths for AV perception tests.
citing papers explorer
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ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Adaptation in Autonomous Driving
Introduces the ASTAD task and training-free ASTModel framework for semantically consistent asymmetric style transfer using labeled synthetic content and unlabeled real references.
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WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence
A real multi-city, multi-kilometer surround-view driving dataset plus an urban-tailored 3DGS baseline shows that city-scale reconstruction still degrades with scale, off-trajectory views, and real-world noise.
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From Nominal Intensity to Equivalent Rainfall: A Path-Based Credibility Evaluation Framework for Simulated Rainfall in Autonomous-Driving Perception Tests
Introduces a path-based credibility framework using equivalent rainfall intensity, RRD scores from real raindrop spectra, and lidar consistency metrics to identify preferable simulated rainfall paths for AV perception tests.