24 papers
Towards Metric-Agnostic Trajectory Forecasting
Markus Knoche, Daan de Geus, Bastian Leibe
Accurate trajectory forecasting of surrounding traffic participants is a core capability for autonomous driving, enabling vehicles to anticipate behavior and plan safe maneuvers. W…
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…
Block-Sparse Global Attention for Efficient Multi-View Geometry Transformers
Chung-Shien Brian Wang, Christian Schmidt, Jens Piekenbrinck +1
Efficient and accurate feed-forward multi-view reconstruction has long been an important task in computer vision. Recent transformer-based models like VGGT, and MapAnything…
Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu +4
Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribut…
Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Dynamics Models
Julia Berger, Bernd Frauenknecht, Sebastian Trimpe +1
Model-based reinforcement learning distinguishes between dynamics models operating on proprioceptive states and latent dynamics models typically operating on high-dimensional image…
Volume Transformer: Revisiting Vanilla Transformers for 3D Scene Understanding
Kadir Yilmaz, Adrian Kruse, Tristan Höfer +2
Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong domain priors. This keeps the field…