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20242026
most citedOpenESS: Event-based Semantic Scene Understanding with Open Vocabularies

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cs.CV2026

EventDrive: Event Cameras for Vision-Language Driving Intelligence

Dongyue Lu, Rong Li, Ao Liang +6

Event cameras sense the world through asynchronous brightness changes with microsecond latency and high dynamic range, offering motion fidelity far beyond frame-based sensors and c…

cs.CV2026

Is Your Driving World Model an All-Around Player?

Lingdong Kong, Ao Liang, Tianyi Yan +20

Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic phys…

cs.CV2026

SCAR-GS: Spatial Context Attention for Residuals in Progressive Gaussian Splatting

Diego Revilla, Pooja Suresh, Ooi Wei Tsang +1

Recent advances in 3D Gaussian Splatting have allowed for real-time, high-fidelity novel view synthesis. Nonetheless, these models have significant storage requirements for large a…

cs.CV2025

Learning to Remove Lens Flare in Event Camera

Haiqian Han, Lingdong Kong, Jianing Li +7

Event cameras have the potential to revolutionize vision systems with their high temporal resolution and dynamic range, yet they remain susceptible to lens flare, a fundamental opt…

cs.CV2025

WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World

Ao Liang, Lingdong Kong, Tianyi Yan +19

Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. D…

cs.CV2025

See4D: Pose-Free 4D Generation via Auto-Regressive Video Inpainting

Dongyue Lu, Ao Liang, Tianxin Huang +8

Immersive applications call for synthesizing spatiotemporal 4D content from casual videos without costly 3D supervision. Existing video-to-4D methods typically rely on manually ann…