8 citations · 9 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
ScanNeRF: a Scalable Benchmark for Neural Radiance Fields
Luca De Luigi, Damiano Bolognini, Federico Domeniconi +3
In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and imple…
Learning the Space of Deep Models
Gianluca Berardi, Luca De Luigi, Samuele Salti +1
Embedding of large but redundant data, such as images or text, in a hierarchy of lower-dimensional spaces is one of the key features of representation learning approaches, which no…