most citedVisual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

2 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2026

DirectFisheye-GS: Enabling Native Fisheye Input in Gaussian Splatting with Cross-View Joint Optimization

Zhengxian Yang, Fei Xie, Xutao Xue +5

3D Gaussian Splatting (3DGS) has enabled efficient 3D scene reconstruction from everyday images with real-time, high-fidelity rendering, greatly advancing VR/AR applications. Fishe…

cs.CV20241 cited

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…

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…

cs.CV20242 cited

Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

Jingyi Xu, Weidong Yang, Lingdong Kong +4

Unsupervised domain adaptation (UDA) is vital for alleviating the workload of labeling 3D point cloud data and mitigating the absence of labels when facing a newly defined domain.…

cs.CV2024

3D Gaussian as a New Era: A Survey

Ben Fei, Jingyi Xu, Rui Zhang +3

3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of Computer Graphics, offering explicit scene representation and novel view synthesis without th…