activity
20222024
most citedDeep Learning on Implicit Neural Representations of Shapes

8 citations · 12 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2024

Connecting NeRFs, Images, and Text

Francesco Ballerini, Pierluigi Zama Ramirez, Roberto Mirabella +2

Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Conc…

cs.CV2024

Test Time Training for Industrial Anomaly Segmentation

Alex Costanzino, Pierluigi Zama Ramirez, Mirko Del Moro +4

Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality control. While existing methods excel in generating anomaly scores for each pixel, practical application…

cs.CV2023

Looking at words and points with attention: a benchmark for text-to-shape coherence

Andrea Amaduzzi, Giuseppe Lisanti, Samuele Salti +1

While text-conditional 3D object generation and manipulation have seen rapid progress, the evaluation of coherence between generated 3D shapes and input textual descriptions lacks…

cs.CV2023

Learning Depth Estimation for Transparent and Mirror Surfaces

Alex Costanzino, Pierluigi Zama Ramirez, Matteo Poggi +3

Inferring the depth of transparent or mirror (ToM) surfaces represents a hard challenge for either sensors, algorithms, or deep networks. We propose a simple pipeline for learning…

cs.CV20233 cited

ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects

Marco Toschi, Riccardo De Matteo, Riccardo Spezialetti +3

In this paper, we focus on the problem of rendering novel views from a Neural Radiance Field (NeRF) under unobserved light conditions. To this end, we introduce a novel dataset, du…

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…