61 citations · 64 across the 9 of their papers we have counts for
5 papers · 1 filter
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
Thomas Monninger, Zihan Zhang, Steffen Staab +1
Accurate environmental representations are essential for autonomous driving, providing the foundation for safe and efficient navigation. Traditionally, high-definition (HD) maps ar…
MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training
Hongyu Lyu, Thomas Monninger, Julie Stephany Berrio Perez +3
Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online construction of HD maps offers a scalable…
MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving
Thomas Monninger, Zihan Zhang, Zhipeng Mo +3
Autonomous driving requires an understanding of the static environment from sensor data. Learned Bird's-Eye View (BEV) encoders are commonly used to fuse multiple inputs, and a vec…
AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
Santosh Vasa, Aditi Ramadwar, Jnana Rama Krishna Darabattula +5
Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple…
AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction
Thomas Monninger, Md Zafar Anwar, Stanislaw Antol +2
Autonomous driving requires understanding infrastructure elements, such as lanes and crosswalks. To navigate safely, this understanding must be derived from sensor data in real-tim…