no code implementations • CVPR 2022 • Norman Müller, Andrea Simonelli, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, Peter Kontschieder
We introduce AutoRF - a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view.
1 code implementation • 7 Feb 2022 • Xinzhu Ma, Wanli Ouyang, Andrea Simonelli, Elisa Ricci
3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years.
no code implementations • 20 Jan 2021 • Angela D. V. Di Virgilio, Umberto Giacomelli, Andrea Simonelli, Giuseppe Terreni, Andrea Basti, Nicolò Beverini, Giorgio Carelli, Donatella Ciampini, Francesco Fuso, Enrico Maccioni, Paolo Marsili, Carlo Altucci, Francesco Bajardi, Salvatore Capozziello, Raffaele Velotta, Alberto Porzio, Antonello Ortolan
The sensitivity to angular rotation of the top class Sagnac gyroscope GINGERINO is carefully investigated with standard statistical means, using 103 days of continuous operation and the available geodesic measurements of the Earth angular rotation rate.
General Relativity and Quantum Cosmology Geophysics
no code implementations • ICCV 2021 • Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Elisa Ricci
Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark, in particular on the commonly reported validation split.
no code implementations • ECCV 2020 • Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Elisa Ricci, Peter Kontschieder
While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches lag much behind.
no code implementations • ICCV 2019 • Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, Peter Kontschieder
In this paper we propose an approach for monocular 3D object detection from a single RGB image, which leverages a novel disentangling transformation for 2D and 3D detection losses and a novel, self-supervised confidence score for 3D bounding boxes.
3D Object Detection From Monocular Images Disentanglement +3