104 citations · 108 across the 7 of their papers we have counts for
6 papers · 1 filter
TASE: Truncation-Aware Semantic Embeddings for 3D Scene Understanding and Editing
Tim-Felix Faasch, Jochen Kall, Lucas Nunes +2
High-fidelity semantic 3D scene representations are crucial for numerous applications, including robotics, autonomous driving, and simulation. Beyond this, the ability to edit such…
SurGe: Improved Surface Geometry in Point Maps
Karim Knaebel, Gonzalo Martin Garcia, Christian Schmidt +4
Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibit inaccurate local surface g…
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…
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…
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
Lucas Nunes, Rodrigo Marcuzzi, Benedikt Mersch +2
Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D…
Open-World Semantic Segmentation Including Class Similarity
Matteo Sodano, Federico Magistri, Lucas Nunes +2
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand thei…