most citedSIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2025

MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction under Various Light Conditions

Qingyuan Zhou, Yuehu Gong, Weidong Yang +6

Novel view synthesis (NVS) and surface reconstruction (SR) are essential tasks in 3D Gaussian Splatting (3D-GS). Despite recent progress, these tasks are often addressed independen…

cs.LG2024★ 1 cited

SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting

Jingyi Xu, Yeqi Luo, Weidong Yang +4

Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities. In the last few years, multiple deep learning bas…

physics.ao-ph2024

IceDiff: High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior

Jingyi Xu, Siwei Tu, Weidong Yang +6

Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scal…

cs.CV2024

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

Keyi Liu, Yeqi Luo, Weidong Yang +4

Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches f…

cs.MM2024

3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering

Qingyuan Zhou, Weidong Yang, Ben Fei +5

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods hav…