fabiotosi92
Junior Assistant Professor (RTDA) at University of Bologna - Computer Science and Engineering
University of BolognaBologna
Pinned Repositories
3net
Repository for "Learning monocular depth estimation with unsupervised trinocular assumptions"
Awesome-Deep-Stereo-Matching
A curated list of awesome Deep Stereo Matching resources
CCNN-Tensorflow
Learning from scratch a confidence measure
Diffusion4RobustDepth
[ECCV 2024] Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions
LGC-Tensorflow
We propose to exploit nearby and farther clues available from image and disparity domains to obtain a more accurate confidence estimation. While local information is very effective for detecting high frequency patterns, it lacks insights from farther regions in the scene. On the other hand, enlarging the receptive field allows to include clues from farther regions but produces smoother uncertainty estimation, not particularly accurate when dealing with high frequency patterns. For these reasons, we propose a multi-stage cascaded network to combine the best of the two worlds.
monoResMatch-Tensorflow
Tensorflow implementation of monocular Residual Matching (monoResMatch) network.
NeRF-Supervised-Deep-Stereo
A novel paradigm for collecting and generating stereo training data using neural rendering
Optical-Tracking-Velocimetry
SMD-Nets
SMD-Nets: Stereo Mixture Density Networks
Unsupervised-Confidence-Measures
This strategy provides labels for training confidence measures based on machine-learning technique without ground-truth labels (BMVC 2017)
fabiotosi92's Repositories
fabiotosi92/NeRF-Supervised-Deep-Stereo
A novel paradigm for collecting and generating stereo training data using neural rendering
fabiotosi92/Awesome-Deep-Stereo-Matching
A curated list of awesome Deep Stereo Matching resources
fabiotosi92/SMD-Nets
SMD-Nets: Stereo Mixture Density Networks
fabiotosi92/monoResMatch-Tensorflow
Tensorflow implementation of monocular Residual Matching (monoResMatch) network.
fabiotosi92/Diffusion4RobustDepth
[ECCV 2024] Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions
fabiotosi92/CCNN-Tensorflow
Learning from scratch a confidence measure
fabiotosi92/Unsupervised-Confidence-Measures
This strategy provides labels for training confidence measures based on machine-learning technique without ground-truth labels (BMVC 2017)
fabiotosi92/LGC-Tensorflow
We propose to exploit nearby and farther clues available from image and disparity domains to obtain a more accurate confidence estimation. While local information is very effective for detecting high frequency patterns, it lacks insights from farther regions in the scene. On the other hand, enlarging the receptive field allows to include clues from farther regions but produces smoother uncertainty estimation, not particularly accurate when dealing with high frequency patterns. For these reasons, we propose a multi-stage cascaded network to combine the best of the two worlds.
fabiotosi92/Optical-Tracking-Velocimetry
fabiotosi92/3net
Repository for "Learning monocular depth estimation with unsupervised trinocular assumptions"
fabiotosi92/fabiotosi92.github.io
GitHub Pages template for academic personal websites.
fabiotosi92/guided-stereo
CVPR 2019 - Guided Stereo Matching
fabiotosi92/PIFu
This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"
fabiotosi92/pydnet
Repository for pydnet, IROS 2018
fabiotosi92/pytorch-ncc
Normalized Cross-Correlation in pytorch
fabiotosi92/Real-time-self-adaptive-deep-stereo
Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL)
fabiotosi92/SfMLearner
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
fabiotosi92/StereoNet
A customized implementation of the paper "StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction"
fabiotosi92/tsdf-fusion-python
Python code to fuse multiple RGB-D images into a TSDF voxel volume.