229 citations · 1k across the 84 of their papers we have counts for
16 papers · 1 filter
Amodal Optical Flow
Maximilian Luz, Rohit Mohan, Ahmed Rida Sekkat +4
Optical flow estimation is very challenging in situations with transparent or occluded objects. In this work, we address these challenges at the task level by introducing Amodal Op…
Panoptic Out-of-Distribution Segmentation
Rohit Mohan, Kiran Kumaraswamy, Juana Valeria Hurtado +2
Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task. However, the performance of pano…
Compositional Servoing by Recombining Demonstrations
Max Argus, Abhijeet Nayak, Martin Büchner +3
Learning-based manipulation policies from image inputs often show weak task transfer capabilities. In contrast, visual servoing methods allow efficient task transfer in high-precis…
Multi-camera Bird's Eye View Perception for Autonomous Driving
David Unger, Nikhil Gosala, Varun Ravi Kumar +3
Most automated driving systems comprise a diverse sensor set, including several cameras, Radars, and LiDARs, ensuring a complete 360°coverage in near and far regions. Unlike Radar…
Few-Shot Panoptic Segmentation With Foundation Models
Markus Käppeler, Kürsat Petek, Niclas Vödisch +2
Current state-of-the-art methods for panoptic segmentation require an immense amount of annotated training data that is both arduous and expensive to obtain posing a significant ch…
RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps
Abhijeet Nayak, Daniele Cattaneo, Abhinav Valada
Localization is paramount for autonomous robots. While camera and LiDAR-based approaches have been extensively investigated, they are affected by adverse illumination and weather c…
Collaborative Dynamic 3D Scene Graphs for Automated Driving
Elias Greve, Martin Büchner, Niclas Vödisch +2
Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building a…
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving
Ahmed Rida Sekkat, Rohit Mohan, Oliver Sawade +2
Unlike humans, who can effortlessly estimate the entirety of objects even when partially occluded, modern computer vision algorithms still find this aspect extremely challenging. L…
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities
Rohit Mohan, José Arce, Sassan Mokhtar +2
Safety and efficiency are paramount in healthcare facilities where the lives of patients are at stake. Despite the adoption of robots to assist medical staff in challenging tasks s…
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales
Christopher Lang, Alexander Braun, Lars Schillingmann +1
Self-supervised multi-object trackers have tremendous potential as they enable learning from raw domain-specific data. However, their re-identification accuracy still falls short c…
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov +5
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a…
Learning and Aggregating Lane Graphs for Urban Automated Driving
Martin Büchner, Jannik Zürn, Ion-George Todoran +2
Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with co…
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields
Julia Hindel, Nikhil Gosala, Kevin Bregler +1
Perception datasets for agriculture are limited both in quantity and diversity which hinders effective training of supervised learning approaches. Self-supervised learning techniqu…
EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
Monish R. Nallapareddy, Kshitij Sirohi, Paulo L. J. Drews-Jr +3
Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decisi…
SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images
Nikhil Gosala, Kürsat Petek, Paulo L. J. Drews-Jr +2
Bird's-Eye-View (BEV) semantic maps have become an essential component of automated driving pipelines due to the rich representation they provide for decision-making tasks. However…
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences
Christopher Lang, Alexander Braun, Lars Schillingmann +2
Self-supervised feature learning enables perception systems to benefit from the vast raw data recorded by vehicle fleets worldwide. While video-level self-supervised learning appro…