most citedGVKF: Gaussian Voxel Kernel Functions for Highly Efficient Surface Reconstruction in Open Scenes

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

collaborators

6 papers

cs.CV2025

VGGT4D: Mining Motion Cues in Visual Geometry Transformers for 4D Scene Reconstruction

Yu Hu, Chong Cheng, Sicheng Yu +2

Reconstructing dynamic 4D scenes is challenging, as it requires robust disentanglement of dynamic objects from the static background. While 3D foundation models like VGGT provide a…

cs.CV2025

Unposed 3DGS Reconstruction with Probabilistic Procrustes Mapping

Chong Cheng, Zijian Wang, Sicheng Yu +3

3D Gaussian Splatting (3DGS) has emerged as a core technique for 3D representation. Its effectiveness largely depends on precise camera poses and accurate point cloud initializatio…

cs.CV2025

Outdoor Monocular SLAM with Global Scale-Consistent 3D Gaussian Pointmaps

Chong Cheng, Sicheng Yu, Zijian Wang +2

3D Gaussian Splatting (3DGS) has become a popular solution in SLAM due to its high-fidelity and real-time novel view synthesis performance. However, some previous 3DGS SLAM methods…

cs.CV2025

RegGS: Unposed Sparse Views Gaussian Splatting with 3DGS Registration

Chong Cheng, Yu Hu, Sicheng Yu +3

3D Gaussian Splatting (3DGS) has demonstrated its potential in reconstructing scenes from unposed images. However, optimization-based 3DGS methods struggle with sparse views due to…

cs.CV2025

Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting

Chong Cheng, Gaochao Song, Yiyang Yao +3

This paper investigates an open research challenge of reconstructing high-quality, large 3D open scenes from images. It is observed existing methods have various limitations, such…

cs.CV20241 cited

GVKF: Gaussian Voxel Kernel Functions for Highly Efficient Surface Reconstruction in Open Scenes

Gaochao Song, Chong Cheng, Hao Wang

In this paper we present a novel method for efficient and effective 3D surface reconstruction in open scenes. Existing Neural Radiance Fields (NeRF) based works typically require e…