513 citations · 2.4k across the 53 of their papers we have counts for
11 papers · 1 filter
Dynamic Visual SLAM using a General 3D Prior
Xingguang Zhong, Liren Jin, Marija Popović +2
Reliable incremental estimation of camera poses and 3D reconstruction is key to enable various applications including robotics, interactive visualization, and augmented reality. Ho…
Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching
Yue Pan, Tao Sun, Liyuan Zhu +4
Point cloud registration aligns multiple unposed point clouds into a common reference frame and is a core step for 3D reconstruction and robot localization without initial guess. I…
Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps
Khanh Son Pham, Christian Witte, Jens Behley +2
Most autonomous cars rely on the availability of high-definition (HD) maps. Current research aims to address this constraint by directly predicting HD map elements from onboard sen…
SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds
Matthias Zeller, Daniel Casado Herraez, Bengisu Ayan +3
Semantic scene understanding, including the perception and classification of moving agents, is essential to enabling safe and robust driving behaviours of autonomous vehicles. Came…
Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds
Matthias Zeller, Vardeep S. Sandhu, Benedikt Mersch +3
The awareness about moving objects in the surroundings of a self-driving vehicle is essential for safe and reliable autonomous navigation. The interpretation of LiDAR and camera da…
Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds
Matthias Zeller, Daniel Casado Herraez, Jens Behley +2
Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, incl…
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…
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…
Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving
Lucas Nunes, Rodrigo Marcuzzi, Jens Behley +1
Semantic scene understanding is crucial for robotics and computer vision applications. In autonomous driving, 3D semantic segmentation plays an important role for enabling safe nav…
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors
Matteo Sodano, Federico Magistri, Elias Marks +6
Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers' decisions on harvesting or precision intervention. Robots can he…
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…