229 citations · 1k across the 84 of their papers we have counts for
88 papers
Leveraging Vision-Based Point Cloud Map Priors for Camera-Based 3D Object Detection and Online Vectorized HD Mapping
Markus Käppeler, Rohit Mohan, Abhinav Valada
Camera-based 3D object detection and online vectorized HD mapping provide compact scene representations for autonomous driving, but both depend on accurate metric geometry and rema…
Mask What Matters: Saliency-Guided Video Self-Supervised Learning for Autonomous Driving
Christopher Lang, Alexander Braun, Abhinav Valada
Video self-supervised learning through masked spatiotemporal prediction has emerged as a promising paradigm for learning feature representations from unlabeled data. However, exist…
Spotted: Location-informed Reidentification of Hyenas and Leopards in Camera Trap Surveys
Halil Sina Kelebek, Julia Hindel, Kobus Hoffman +9
Animal re-identification (ReID) in camera-trap surveys remains challenging due to low image quality, strong variation in illumination and viewpoint, and highly imbalanced numbers o…
Streaming Gaussian Encoding for 4D Panoptic Occupancy Tracking
Maximilian Luz, Thomas Nürnberg, Yakov Miron +1
Camera-based 4D panoptic occupancy tracking (4D-POT) is a promising paradigm for holistic scene understanding from multi-view imagery, enabling joint reasoning about geometry, sema…
Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences
Simon Bultmann, Daniele Cattaneo, Abhinav Valada
Accurate camera-LiDAR calibration is a prerequisite for robust multi-modal perception in robotics. Recent target-less approaches based on deep point correspondences achieve remarka…
AnchorD: Metric Grounding of Monocular Depth Using Factor Graphs
Simon Dorer, Martin Büchner, Nick Heppert +1
Dense and accurate depth estimation is essential for robotic manipulation, grasping, and navigation, yet currently available depth sensors are prone to errors on transparent, specu…
Hyp2Former: Hierarchy-Aware Hyperbolic Embeddings for Open-Set Panoptic Segmentation
Yao Lu, Rohit Mohan, Florian Drews +2
Recognizing unknown objects is crucial for safety-critical applications such as autonomous driving and robotics. Open-Set Panoptic Segmentation (OPS) aims to segment known thing an…
Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking
Markus Käppeler, Özgün Çiçek, Yakov Miron +1
Camera-based 3D object detection and tracking are central to autonomous driving, yet precise 3D object localization remains fundamentally constrained by depth ambiguity when no exp…
Multi-Modal Sensor Fusion using Hybrid Attention for Autonomous Driving
Mayank Mayank, Bharanidhar Duraisamy, Florian Geiß +1
Accurate 3D object detection for autonomous driving requires complementary sensors. Cameras provide dense semantics but unreliable depth, while millimeter-wave radar offers precise…
Assessing Multimodal Chronic Wound Embeddings with Expert Triplet Agreement
Fabian Kabus, Julia Hindel, Jelena Bratulić +6
Recessive dystrophic epidermolysis bullosa (RDEB) is a rare genetic skin disorder for which clinicians greatly benefit from finding similar cases using images and clinical text. Ho…
Embedding interpretable -regression into neural networks for uncovering temporal structure in cell imaging
Fabian Kabus, Maren Hackenberg, Julia Hindel +6
While artificial neural networks excel in unsupervised learning of non-sparse structure, classical statistical regression techniques offer better interpretability, in particular wh…
Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving
Sharang Kaul, Simon Bultmann, Mario Berk +1
Criticality metrics such as time-to-collision (TTC) quantify collision urgency but do not distinguish the operational consequences of false-positive (FP) and false-negative (FN) pe…
ForecastOcc: Vision-based Semantic Occupancy Forecasting
Riya Mohan, Juana Valeria Hurtado, Rohit Mohan +1
Autonomous driving requires forecasting both geometry and semantics over time to effectively reason about future environment states. Existing vision-based occupancy forecasting met…
Latent Gaussian Splatting for 4D Panoptic Occupancy Tracking
Maximilian Luz, Rohit Mohan, Thomas Nürnberg +3
Capturing 4D spatiotemporal scene structure is crucial for the safe and reliable operation of robots in dynamic environments. However, existing approaches typically address only pa…
DOS: Distilling Observable Softmaps of Zipfian Prototypes for Self-Supervised Point Representation
Mohamed Abdelsamad, Michael Ulrich, Bin Yang +3
Recent advances in self-supervised learning (SSL) have shown tremendous potential for learning 3D point cloud representations without human annotations. However, SSL for 3D point c…
Bridging Perspectives: Foundation Model Guided BEV Maps for 3D Object Detection and Tracking
Markus Käppeler, Özgün Çiçek, Daniele Cattaneo +3
Camera-based 3D object detection and tracking are essential for perception in autonomous driving. Current state-of-the-art approaches often rely exclusively on either perspective-v…
Hyperspectral Adapter for Semantic Segmentation with Vision Foundation Models
Juana Valeria Hurtado, Rohit Mohan, Abhinav Valada
Hyperspectral imaging (HSI) captures spatial information along with dense spectral measurements across numerous narrow wavelength bands. This rich spectral content has the potentia…
Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation
Julia Hindel, Ema Mekic, Enamundram Naga Karthik +4
Robot-assisted surgeries rely on accurate and real-time scene understanding to safely guide surgical instruments. However, segmentation models trained on static datasets face key l…
Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning
Rohit Mohan, Julia Hindel, Florian Drews +3
Autonomous vehicles that navigate in open-world environments may encounter previously unseen object classes. However, most existing LiDAR panoptic segmentation models rely on close…
Contour Errors: Ego-Centric Matching for 3D Multi-Object Tracking Performance Evaluation
Sharang Kaul, Simon Bultmann, Mario Berk +1
Open-loop performance evaluation of 3D multi-object tracking in autonomous driving requires matching criteria that effectively penalize translational, shape, and orientation errors…
Visual Loop Closure Detection Through Deep Graph Consensus
Martin Büchner, Liza Dahiya, Simon Dorer +4
Visual loop closure detection traditionally relies on place recognition methods to retrieve candidate loops that are validated using computationally expensive RANSAC-based geometri…
ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models
Jiarong Wei, Niclas Vödisch, Anna Rehr +2
Automated parking is a critical feature of Advanced Driver Assistance Systems (ADAS), where accurate trajectory prediction is essential to bridge perception and planning modules. D…
Learning Appearance and Motion Cues for Panoptic Tracking
Juana Valeria Hurtado, Sajad Marvi, Rohit Mohan +1
Panoptic tracking enables pixel-level scene interpretation of videos by integrating instance tracking in panoptic segmentation. This provides robots with a spatio-temporal understa…
Collaborative Dynamic 3D Scene Graphs for Open-Vocabulary Urban Scene Understanding
Tim Steinke, Martin Büchner, Niclas Vödisch +1
Mapping and scene representation are fundamental to reliable planning and navigation in mobile robots. While purely geometric maps using voxel grids allow for general navigation, o…