87 citations · 122 across the 16 of their papers we have counts for
25 papers
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez, Alex Costanzino, Fabio Tosi +4
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open…
Self-Distillation for Unsupervised 3D Domain Adaptation
Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez +2
Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as…
Cross-Spectral Neural Radiance Fields
Matteo Poggi, Pierluigi Zama Ramirez, Fabio Tosi +3
We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural R…
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation
Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi +3
We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purpos…
Open Challenges in Deep Stereo: the Booster Dataset
Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi +3
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the p…
Neural Disparity Refinement for Arbitrary Resolution Stereo
Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez +4
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile ph…