50 citations · 53 across the 6 of their papers we have counts for
13 papers
HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors
Hyungtae Lim, Seoyeon Jang, Benedikt Mersch +3
Moving object segmentation (MOS) using a 3D light detection and ranging (LiDAR) sensor is crucial for scene understanding and identification of moving objects. Despite the availabi…
Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation
Balázs Opra, Betty Le Dem, Jeffrey M. Walls +2
Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This pap…
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…
STAIR: Semantic-Targeted Active Implicit Reconstruction
Liren Jin, Haofei Kuang, Yue Pan +2
Many autonomous robotic applications require object-level understanding when deployed. Actively reconstructing objects of interest, i.e. objects with specific semantic meanings, is…
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…
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Semantic segmentation enables robots to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots…