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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

fabiotosi92.github.io

GitHub Pages template for academic personal websites.

guided-stereo

CVPR 2019 - Guided Stereo Matching

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

pifu

This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"

pydnet

Repository for pydnet, IROS 2018

pytorch-ncc

Normalized Cross-Correlation in pytorch

real-time-self-adaptive-deep-stereo

Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL)

sfmlearner

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

smd-nets

SMD-Nets: Stereo Mixture Density Networks

stereonet

A customized implementation of the paper "StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction"

tsdf-fusion-python

Python code to fuse multiple RGB-D images into a TSDF voxel volume.

unsupervised-confidence-measures

This strategy provides labels for training confidence measures based on machine-learning technique without ground-truth labels (BMVC 2017)