1 citations · 1 across the 3 of their papers we have counts for
7 papers
What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models
Tianchen Deng, Yue Pan, Shenghai Yuan +10
In this paper, we provide a comprehensive overview of existing scene representation methods for robotics, covering traditional representations such as point clouds, voxels, signed…
Globally Consistent RGB-D SLAM with 2D Gaussian Splatting
Xingguang Zhong, Yue Pan, Liren Jin +3
Recently, 3D Gaussian splatting-based RGB-D SLAM displays remarkable performance of high-fidelity 3D reconstruction. However, the lack of depth rendering consistency and efficient…
VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes
Tianchen Deng, Wenhua Wu, Junjie He +4
3D Gaussian Splatting has recently shown promising results in dense visual SLAM. However, existing 3DGS-based SLAM methods are all constrained to small-room scenarios and struggle…
Improving Indoor Localization Accuracy by Using an Efficient Implicit Neural Map Representation
Haofei Kuang, Yue Pan, Xingguang Zhong +3
Globally localizing a mobile robot in a known map is often a foundation for enabling robots to navigate and operate autonomously. In indoor environments, traditional Monte Carlo lo…
PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map
Yue Pan, Xingguang Zhong, Liren Jin +4
Robots benefit from high-fidelity reconstructions of their environment, which should be geometrically accurate and photorealistic to support downstream tasks. While this can be ach…
ActiveGS: Active Scene Reconstruction Using Gaussian Splatting
Liren Jin, Xingguang Zhong, Yue Pan +3
Robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene…