most citedDeep Learning on Implicit Neural Representations of Shapes

8 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

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…

cs.CV2024

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…

cs.CV2023

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…

cs.CV20238 cited

Deep Learning on Implicit Neural Representations of Shapes

Luca De Luigi, Adriano Cardace, Riccardo Spezialetti +3

Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D…

cs.CV2023

Learning Good Features to Transfer Across Tasks and Domains

Pierluigi Zama Ramirez, Adriano Cardace, Luca De Luigi +3

Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to…