93 citations · 295 across the 16 of their papers we have counts for
8 papers · 1 filter
UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation
Rohit Mohan, Florian Drews, Yakov Miron +2
LiDAR-camera fusion enhances 3D panoptic segmentation by leveraging camera images to complement sparse LiDAR scans, but it also introduces a critical failure mode. Under adverse co…
CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration
Daniele Cattaneo, Abhinav Valada
LiDARs are widely used for mapping and localization in dynamic environments. However, their high cost limits their widespread adoption. On the other hand, monocular localization in…
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…
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation
Borna Bešić, Nikhil Gosala, Daniele Cattaneo +1
Scene understanding is a pivotal task for autonomous vehicles to safely navigate in the environment. Recent advances in deep learning enable accurate semantic reconstruction of the…
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
Daniele Cattaneo, Domenico Giorgio Sorrenti, Abhinav Valada
Localization is a critically essential and crucial enabler of autonomous robots. While deep learning has made significant strides in many computer vision tasks, it is still yet to…
Global visual localization in LiDAR-maps through shared 2D-3D embedding space
Daniele Cattaneo, Matteo Vaghi, Simone Fontana +2
Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonom…
CMRNet: Camera to LiDAR-Map Registration
Daniele Cattaneo, Matteo Vaghi, Augusto Luis Ballardini +3
In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not…
A dataset for benchmarking vision-based localization at intersections
Augusto L. Ballardini, Daniele Cattaneo, Domenico G. Sorrenti
In this report we present the work performed in order to build a dataset for benchmarking vision-based localization at intersections, i.e., a set of stereo video sequences taken fr…