🧮 Scientific Computing

📷 CVPR2026 · 4 paper notes

Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesis

This paper proposes an exposure-time-dependent modulation transfer function (ET-MTF) that treats exposure time as a continuous variable, and constructs a large-scale synthetic turbulence dataset ET-Turb (5,083 videos, 2 million frames), significantly improving the generalization of turbulence restoration models on real-world data.

EHETM: High-Quality and Efficient Turbulence Mitigation with Events

This paper proposes EHETM, the first method to leverage the microsecond temporal resolution of event cameras to overcome the accuracy–efficiency bottleneck of conventional multi-frame turbulence mitigation (TM). Two key physical phenomena are identified—polarity alternation of turbulence-induced events correlated with image gradients, and spatiotemporally coherent "event tubes" formed by dynamic objects—motivating two complementary modules: a polarity-weighted gradient module and an event tube constraint module. EHETM reduces data overhead by 77.3% and system latency by 89.5%, with particularly substantial gains over the state of the art in dynamic-object scenes.

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

NESTOR, a nested MoE-based neural operator, is proposed to capture global features across different PDE types via image-level MoE and local spatial correlations within physical fields via token-level Sub-MoE. The model is pre-trained on 12 PDE datasets and effectively transferred to downstream tasks.

PhysSkin: Real-Time and Generalizable Physics-Based Skin Simulation

PhysSkin is a generalizable physics-informed framework that learns continuous skinning weight fields directly from static 3D geometry via a neural skinning field autoencoder, coupled with a physics-informed self-supervised learning strategy (energy minimization + smoothness + orthogonality constraints), enabling real-time physics-based animation that generalizes across shapes and discretizations without any annotated data or simulation trajectories.