17 citations · 17 across the 11 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…
Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters
Julia Hindel, Rohit Mohan, Jelena Bratulic +3
LiDAR semantic segmentation models are typically trained from random initialization as universal pre-training is hindered by the lack of large, diverse datasets. Moreover, most poi…
Progressive Multi-Modal Fusion for Robust 3D Object Detection
Rohit Mohan, Daniele Cattaneo, Florian Drews +1
Multi-sensor fusion is crucial for accurate 3D object detection in autonomous driving, with cameras and LiDAR being the most commonly used sensors. However, existing methods perfor…
Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception
Julia Hindel, Daniele Cattaneo, Abhinav Valada
Semantic segmentation models are typically trained on a fixed set of classes, limiting their applicability in open-world scenarios. Class-incremental semantic segmentation aims to…