3 citations · 5 across the 21 of their papers we have counts for
6 papers · 1 filter
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
Runnan Chen, Zhaoqing Wang, Jiepeng Wang +4
Understanding geometric, semantic, and instance information in 3D scenes from sequential video data is essential for applications in robotics and augmented reality. However, existi…
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies
Runnan Chen, Xiangyu Sun, Zhaoqing Wang +8
Open-vocabulary scene understanding using 3D Gaussian (3DGS) representations has garnered considerable attention. However, existing methods mostly lift knowledge from large 2D visi…
Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels
Haodong Chen, Runnan Chen, Qiang Qu +4
Recent advancements in 3D Gaussian Splatting (3DGS) have substantially improved novel view synthesis, enabling high-quality reconstruction and real-time rendering. However, blurrin…
UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction
Ziwen Li, Jiaxin Huang, Runnan Chen +5
Reconstructing urban scenes is challenging due to their complex geometries and the presence of potentially dynamic objects. 3D Gaussian Splatting (3DGS)-based methods have shown st…
LaVin-DiT: Large Vision Diffusion Transformer
Zhaoqing Wang, Xiaobo Xia, Runnan Chen +4
This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative fra…
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
Jiahao Sun, Chunmei Qing, Xiang Xu +10
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on d…