260 citations · 327 across the 61 of their papers we have counts for
13 papers · 1 filter
ScanNeRF: a Scalable Benchmark for Neural Radiance Fields
Luca De Luigi, Damiano Bolognini, Federico Domeniconi +3
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 imple…
MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation
Jongbeom Baek, Gyeongnyeon Kim, Seonghoon Park +3
We propose MaskingDepth, a novel semi-supervised learning framework for monocular depth estimation to mitigate the reliance on large ground-truth depth quantities. MaskingDepth is…
Sparsity Agnostic Depth Completion
Andrea Conti, Matteo Poggi, Stefano Mattoccia
We present a novel depth completion approach agnostic to the sparsity of depth points, that is very likely to vary in many practical applications. State-of-the-art approaches yield…
The Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Chris Russell +16
This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular…
TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network
Youmin Zhang, Matteo Poggi, Stefano Mattoccia
We present TemporalStereo, a coarse-to-fine stereo matching network that is highly efficient, and able to effectively exploit the past geometry and context information to boost mat…
Multi-View Guided Multi-View Stereo
Matteo Poggi, Andrea Conti, Stefano Mattoccia
This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acqui…
Unsupervised confidence for LiDAR depth maps and applications
Andrea Conti, Matteo Poggi, Filippo Aleotti +1
Depth perception is pivotal in many fields, such as robotics and autonomous driving, to name a few. Consequently, depth sensors such as LiDARs rapidly spread in many applications.…
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…
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
Chaoqiang Zhao, Youmin Zhang, Matteo Poggi +6
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth labels for training. Convolutional neural networks (CNNs) have recen…
Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions
Theodoros Panagiotakopoulos, Pier Luigi Dovesi, Linus Härenstam-Nielsen +1
Unsupervised Domain Adaptation (UDA) aims at reducing the domain gap between training and testing data and is, in most cases, carried out in offline manner. However, domain changes…
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…
Monitoring social distancing with single image depth estimation
Alessio Mingozzi, Andrea Conti, Filippo Aleotti +2
The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people result…