mattpoggi

footprints

[CVPR 2020] Estimation of the visible and hidden traversable space from a single color image

stereo-from-mono

[ECCV 2020] Learning stereo from single images using monocular depth estimation networks

DenseMatchingBenchmark

Dense Matching Benchmark

AcfNet

[AAAI2020] Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

Reversing

Code for "Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation"

DepthComplete

Pytorch implementation of depth completion architectures (eg. SparseConv, Sparse-to-Dense)

demo_live

You can try the demo here:

omeganet

Distilled Semantics for Comprehensive Scene Understanding from Videos [CVPR 2020]

netdef-docker

DispNet3, FlowNet3, FlowNetH, SceneFlowNet -- in Docker

depth-hints

[ICCV 2019] Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs

ICCV_19

Federica Arrigoni and Tomas Pajdla. Robust Motion Segmentation from Pairwise Matches. ICCV 2019

DSMNet

Domain-invariant Stereo Matching Networks

DWARF-Tensorflow

TensorFlow implementation of "Learning end-to-end scene flow by distilling single tasks knowledge"

flowattack

Attacking Optical Flow (ICCV 2019)

briefmatch

BriefMatch real-time GPU optical flow

optical-flow-filter

A real time optical flow algorithm implemented on GPU

ATDT

Implementation of "Learning Across Tasks and Domains" ICCV 2019

d2-net

D2-Net: A Trainable CNN for Joint Description and Detection of Local Features

mobilePydnet

Pydnet on mobile devices

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.

Learning2AdaptForStereo

Code for: "Learning To Adapt For Stereo" accepted at CVPR2019

monoResMatch-Tensorflow

Tensorflow implementation of monocular Residual Matching (monoResMatch) network.

Semantic-Mono-Depth

Geometry meets semantics for semi-supervised monocular depth estimation - ACCV 2018

Real-time-self-adaptive-deep-stereo

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

pydnet

Repository for pydnet, IROS 2018

lautaro

Unsupervised-Confidence-Measures

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

Unsupervised-Adaptation-for-Deep-Stereo

Code for "Unsupervised Adaptation for Deep Stereo" - ICCV17

CrossScaleStereo

Cross-Scale Cost Aggregation for Stereo Matching (CVPR 2014)

models

Models and examples built with TensorFlow