⚛️ Physics & Scientific Computing

🎞️ ECCV2026 · 5 paper notes

📌 Same area in other venues: 📷 CVPR2026 (2) · 🔬 ICLR2026 (69) · 🧪 ICML2026 (33) · 🤖 AAAI2026 (15) · 🧠 NeurIPS2025 (57) · 📹 ICCV2025 (2)

High-speed Imaging through Turbulence with Event-based Light Fields

Addressing the severe motion blur and geometric wobble when imaging dynamic scenes through atmospheric turbulence, this work presents the first event-only light field imaging system. By exploiting kaleidoscope optics to capture 9 sub-aperture views, it physically decouples cross-view correlated scene motion from uncorrelated turbulence perturbations, enabling crystal-clear video reconstruction at up to 12,000 fps under strong turbulence.

Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT

This paper introduces Computed Tomography neural Operator (CTO), the first neural operator framework for sparse-view CT reconstruction that utilizes DISCO continuous convolutions and dual-domain spatial-frequency learning to achieve high-fidelity zero-shot generalization across measurement sampling rates and grid resolutions.

ELHINN: Unifying Dense Crowd Simulation Across Scales via Eulerian–Lagrangian Hydrodynamics

To bridge the fundamental disconnect between macroscopic collective coherence and microscopic individual realism in dense crowd simulation, ELHINN introduces a cross-scale hydrodynamics framework where an enhanced Eulerian velocity model (HINN++) guides a microscopic Lagrangian trajectory network (PINN) augmented with KAN residual correction and entrance-aware resampling.

Event-based Sparse-view Background-Oriented Schlieren Tomography

Addressing the high data bandwidth and intense illumination demands of frame-based background-oriented schlieren (BOS) tomography, this paper proposes the first event-based sparse-view BOS tomography framework (EventBOS), coupling a continuous 4D neural implicit fluid field with differentiable refractive ray tracing and thermal-convection Navier-Stokes regularization to reconstruct high-speed airflow dynamics that outperform 1000 fps frame-based baselines under single- and orthogonal dual-view setups.

TurboMPLE: Joint Infrared Turbulence Mitigation and Physical Fields Estimation via Mutual Progressive Layered Extraction

To tackle optical distortions, blur, and grayscale drift induced by atmospheric turbulence in thermal infrared imaging, TurboMPLE establishes an end-to-end multi-task learning framework that achieves bidirectional feature-level mutual guidance and physical law constraints, jointly delivering state-of-the-art video turbulence mitigation and accurate 2D dynamic physical fields estimation.