11 citations · 13 across the 12 of their papers we have counts for
13 papers
GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction
Yedong Shen, Shiqi Zhang, Sha Zhang +6
Reconstructing static 3D scene from monocular video with dynamic objects is important for numerous applications such as virtual reality and autonomous driving. Current approaches t…
GS-Share: Enabling High-fidelity Map Sharing with Incremental Gaussian Splatting
Xinran Zhang, Hanqi Zhu, Yifan Duan +1
Constructing and sharing 3D maps is essential for many applications, including autonomous driving and augmented reality. Recently, 3D Gaussian splatting has emerged as a promising…
UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
Hanqi Zhu, Wuyang Zhang, Xinran Zhang +5
The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary natur…
SpatialSplat: Efficient Semantic 3D from Sparse Unposed Images
Yu Sheng, Jiajun Deng, Xinran Zhang +4
A major breakthrough in 3D reconstruction is the feedforward paradigm to generate pixel-wise 3D points or Gaussian primitives from sparse, unposed images. To further incorporate se…
STDArm: Transferring Visuomotor Policies From Static Data Training to Dynamic Robot Manipulation
Yifan Duan, Heng Li, Yilong Wu +5
Recent advances in mobile robotic platforms like quadruped robots and drones have spurred a demand for deploying visuomotor policies in increasingly dynamic environments. However,…
MT-PCR: Leveraging Modality Transformation for Large-Scale Point Cloud Registration with Limited Overlap
Yilong Wu, Yifan Duan, Yuxi Chen +5
Large-scale scene point cloud registration with limited overlap is a challenging task due to computational load and constrained data acquisition. To tackle these issues, we propose…
OG-Gaussian: Occupancy Based Street Gaussians for Autonomous Driving
Yedong Shen, Xinran Zhang, Yifan Duan +5
Accurate and realistic 3D scene reconstruction enables the lifelike creation of autonomous driving simulation environments. With advancements in 3D Gaussian Splatting (3DGS), previ…
Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing
Xinran Zhang, Hanqi Zhu, Yifan Duan +5
Constructing precise 3D maps is crucial for the development of future map-based systems such as self-driving and navigation. However, generating these maps in complex environments,…
CELLmap: Enhancing LiDAR SLAM through Elastic and Lightweight Spherical Map Representation
Yifan Duan, Xinran Zhang, Yao Li +4
SLAM is a fundamental capability of unmanned systems, with LiDAR-based SLAM gaining widespread adoption due to its high precision. Current SLAM systems can achieve centimeter-level…
Rotation Initialization and Stepwise Refinement for Universal LiDAR Calibration
Yifan Duan, Xinran Zhang, Guoliang You +8
Autonomous systems often employ multiple LiDARs to leverage the integrated advantages, enhancing perception and robustness. The most critical prerequisite under this setting is the…
MM-Gaussian: 3D Gaussian-based Multi-modal Fusion for Localization and Reconstruction in Unbounded Scenes
Chenyang Wu, Yifan Duan, Xinran Zhang +3
Localization and mapping are critical tasks for various applications such as autonomous vehicles and robotics. The challenges posed by outdoor environments present particular compl…
OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving
Heng Li, Yifan Duan, Xinran Zhang +3
Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a no…
USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots
Ziming Wang, Yujiang Liu, Yifan Duan +5
In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for…