48 citations · 48 across the 7 of their papers we have counts for
8 papers
Learning Spectral and Polarimetric Clues for One-to-Multimodal Novel View Synthesis
Federico Lincetto, Gianluca Agresti, Mattia Rossi +2
Neural rendering techniques allow for accurate reconstruction of the geometry and color appearance of 3D scenes. Some methods have extended their use to additional imaging modaliti…
MultimodalStudio: A Heterogeneous Sensor Dataset and Framework for Neural Rendering across Multiple Imaging Modalities
Federico Lincetto, Gianluca Agresti, Mattia Rossi +1
Neural Radiance Fields (NeRF) have shown impressive performances in the rendering of 3D scenes from arbitrary viewpoints. While RGB images are widely preferred for training volume…
NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data
Matteo Caligiuri, Adriano Simonetto, Pietro Zanuttigh
The acquisition of objects outside the Line-of-Sight of cameras is a very intriguing but also extremely challenging research topic. Recent works showed the feasibility of this idea…
Exploiting Multiple Priors for Neural 3D Indoor Reconstruction
Federico Lincetto, Gianluca Agresti, Mattia Rossi +1
Neural implicit modeling permits to achieve impressive 3D reconstruction results on small objects, while it exhibits significant limitations in large indoor scenes. In this work, w…
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps
Xiaowen Jiang, Valerio Cambareri, Gianluca Agresti +4
Sparse active illumination enables precise time-of-flight depth sensing as it maximizes signal-to-noise ratio for low power budgets. However, depth completion is required to produc…
Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction
Adriano Simonetto, Gianluca Agresti, Pietro Zanuttigh +1
Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide depth images at an interactive frame rate. However, they are affected by different error sources, with the…
Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
Marco Toldo, Umberto Michieli, Gianluca Agresti +1
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use…
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation
Umberto Michieli, Matteo Biasetton, Gianluca Agresti +1
Deep learning techniques have been widely used in autonomous driving systems for the semantic understanding of urban scenes. However, they need a huge amount of labeled data for tr…