66 citations · 104 across the 9 of their papers we have counts for
11 papers
FlowSim: Rendering-Free Closed-Loop Simulation for Evaluating End-to-End Driving Systems
Hanfeng Wu, Alvaro Marcos-Ramiro, Michael Schmidt +1
Closed-loop evaluation of end-to-end (E2E) driving systems typically relies on generating sensor observations and re-processing them through the perception encoder, introducing sub…
CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding
Andrea Ceron, Michael Schmidt, Alvaro Marcos-Ramiro +2
Latent LiDAR pipelines suffer from flying pixels: convolutional VAEs blur sharp radial depth discontinuities, yielding edge depths that back-project to points floating between surf…
Closing the Navigation Compliance Gap in End-to-end Autonomous Driving
Hanfeng Wu, Marlon Steiner, Michael Schmidt +2
Trajectory-scoring planners achieve high navigation compliance when following the expert's original command, yet they struggle at intersections when presented with alternative comm…
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro +4
Since real-world training datasets cannot properly sample the long tail of the underlying data distribution, corner cases and rare out-of-domain samples can severely hinder the per…
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning
Aral Hekimoglu, Michael Schmidt, Alvaro Marcos-Ramiro
We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected dat…
Active Learning for Object Detection with Non-Redundant Informative Sampling
Aral Hekimoglu, Adrian Brucker, Alper Kagan Kayali +2
Curating an informative and representative dataset is essential for enhancing the performance of 2D object detectors. We present a novel active learning sampling strategy that addr…
Multi-Task Consistency for Active Learning
Aral Hekimoglu, Philipp Friedrich, Walter Zimmer +3
Learning-based solutions for vision tasks require a large amount of labeled training data to ensure their performance and reliability. In single-task vision-based settings, inconsi…
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt +3
Panoptic segmentation methods assign a known class to each pixel given in input. Even for state-of-the-art approaches, this inevitably enforces decisions that systematically lead t…
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro +5
As 3D object detection on point clouds relies on the geometrical relationships between the points, non-standard object shapes can hinder a method's detection capability. However, i…
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Stefano Gasperini, Jan Haug, Mohammad-Ali Nikouei Mahani +4
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In perception for autonomous driving, measuring the uncertainty means providing…
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds
Stefano Gasperini, Mohammad-Ali Nikouei Mahani, Alvaro Marcos-Ramiro +2
Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and e…