Zobrazeno 1 - 10
of 35
pro vyhledávání: '"De Gregorio, Daniele"'
Autor:
Toschi, Marco, De Matteo, Riccardo, Spezialetti, Riccardo, De Gregorio, Daniele, Di Stefano, Luigi, Salti, Samuele
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, dubbed ReNe (Relighting NeRF), framing real world objects under one-ligh
Externí odkaz:
http://arxiv.org/abs/2304.10448
We introduce a novel framework for training deep stereo networks effortlessly and without any ground-truth. By leveraging state-of-the-art neural rendering solutions, we generate stereo training data from image sequences collected with a single handh
Externí odkaz:
http://arxiv.org/abs/2303.17603
Autor:
De Luigi, Luca, Bolognini, Damiano, Domeniconi, Federico, De Gregorio, Daniele, Poggi, Matteo, Di Stefano, Luigi
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 implement an effective pipeline for scanning real objects in quantity and e
Externí odkaz:
http://arxiv.org/abs/2211.13762
We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization. Photo-realistic images of procedurally generated candies are rendered in a controlled environment under multiple lightning conditions, also providin
Externí odkaz:
http://arxiv.org/abs/2210.04570
In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications. When a deep-learning based solution is proposed, usually lacks of any simple method to generate the training
Externí odkaz:
http://arxiv.org/abs/2012.13210
Autor:
Ramirez, Pierluigi Zama, Paternesi, Claudio, De Luigi, Luca, Lella, Luigi, De Gregorio, Daniele, Di Stefano, Luigi
Availability of a few, large-size, annotated datasets, like ImageNet, Pascal VOC and COCO, has lead deep learning to revolutionize computer vision research by achieving astonishing results in several vision tasks.We argue that new tools to facilitate
Externí odkaz:
http://arxiv.org/abs/1910.05021
In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method which leverages on moving a 2D camera by means of a robot, proving precise camera tracking, and an augmented reality pen to define initial object bound
Externí odkaz:
http://arxiv.org/abs/1908.01862
Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation
While robotic manipulation of rigid objects is quite straightforward, coping with deformable objects is an open issue. More specifically, tasks like tying a knot, wiring a connector or even surgical suturing deal with the domain of Deformable Linear
Externí odkaz:
http://arxiv.org/abs/1810.04461
We present a novel mapping framework for robot navigation which features a multi-level querying system capable to obtain rapidly representations as diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These are inherently embedded i
Externí odkaz:
http://arxiv.org/abs/1704.05832
Autor:
Chiaravalli, Davide, Califano, Federico, Biagiotti, Luigi, De Gregorio, Daniele, Melchiorri, Claudio
Publikováno v:
In IFAC PapersOnLine 2018 51(22):306-311