1 citations · 2 across the 7 of their papers we have counts for
10 papers · 1 filter
CalibBEV: LiDAR-Camera Calibration via BEV Alignment
Filippo D'Addeo, Lorenzo Cipelli, Adriano Cardace +3
We present CalibBEV, a novel Bird's Eye View (BEV) alignment approach for LiDAR-camera calibration. Our method unifies LiDAR and camera data into a shared 3D spatial representation…
RETR: Multi-View Radar Detection Transformer for Indoor Perception
Ryoma Yataka, Adriano Cardace, Pu Perry Wang +2
Indoor radar perception has seen rising interest due to affordable costs driven by emerging automotive imaging radar developments and the benefits of reduced privacy concerns and r…
MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception
M. Mahbubur Rahman, Ryoma Yataka, Sorachi Kato +4
Compared with an extensive list of automotive radar datasets that support autonomous driving, indoor radar datasets are scarce at a smaller scale in the format of low-resolution ra…
Deep Learning on Object-centric 3D Neural Fields
Pierluigi Zama Ramirez, Luca De Luigi, Daniele Sirocchi +5
In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes. When applied to 3D data…
Neural Processing of Tri-Plane Hybrid Neural Fields
Adriano Cardace, Pierluigi Zama Ramirez, Francesco Ballerini +3
Driven by the appealing properties of neural fields for storing and communicating 3D data, the problem of directly processing them to address tasks such as classification and part…
Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation
Adriano Cardace, Pierluigi Zama Ramirez, Samuele Salti +1
3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality. However, the task is extremely challenging due…