1 citations · 2 across the 5 of their papers we have counts for
6 papers
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting
Keyi Liu, Weidong Yang, Ben Fei +1
Self-supervised learning (SSL) for point cloud pre-training has become a cornerstone for many 3D vision tasks, enabling effective learning from large-scale unannotated data. At the…
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…
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…
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…
RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering
Rui Zhang, Tianyue Luo, Weidong Yang +5
3D Gaussian Splatting (3D-GS) has made a notable advancement in the field of neural rendering, 3D scene reconstruction, and novel view synthesis. Nevertheless, 3D-GS encounters the…
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…