🧮 Scientific Computing

📷 CVPR2026 · 3 paper notes

Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesis

Ours proposes the Exposure-Time dependent Modulation Transfer Function (ET-MTF), modeling exposure time as a continuous variable. A large-scale synthetic turbulence dataset, ET-Turb (5,083 videos, 2 million frames), is constructed, significantly improving the generalization of turbulence restoration models on real-world data.

EHETM: High-Quality and Efficient Turbulence Mitigation with Events

EHETM is proposed as the first method to leverage the microsecond temporal resolution of event cameras to break the accuracy-efficiency bottleneck of traditional multi-frame Turbulence Mitigation (TM). By discovering two key physical phenomena—the correlation between polarity alternation of turbulence-induced events and sharp gradients, and the formation of spatio-temporally coherent "event tubes" by dynamic objects—the authors design the Polarity-Weighted Gradient and Event Tube Constraint modules. EHETM reduces data overhead by 77.3% and system latency by 89.5%, significantly surpassing SOTA methods, especially in dynamic scenes.

NESTOR: A Nested MOE-based Neural Operator for Large-Scale PDE Pre-Training

Ours proposes NESTOR, a nested MoE neural operator. It captures global features of different PDE types through image-level MoE and local correlations within physical fields through token-level Sub-MoE. It achieves large-scale pre-training across 12 PDE datasets and effectively transfers to downstream tasks.