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Rémi Pautrat's Projects

d2_net icon d2_net

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

dl_framework icon dl_framework

Personal Pytorch framework to train and run deep learning algorithms

dsacstar icon dsacstar

DSAC* for Visual Camera Re-Localization (RGB or RGB-D)

gift icon gift

Code for "GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs" NeurIPS 2019

glue-factory icon glue-factory

Training library for local feature detection and matching

hawp icon hawp

Holistically-Attracted Wireframe Parsing

homography_est icon homography_est

Light-weight library to perform homography estimation with RANSAC from point, line or point-line correspondences

kornia icon kornia

Open Source Differentiable Computer Vision Library

lisrd icon lisrd

Local Invariance Selection at Runtime for Descriptors (LISRD)

oanet icon oanet

Implementation of ICCV19 Paper "Learning Two-View Correspondences and Geometry Using Order-Aware Network"

open3d icon open3d

Open3D: A Modern Library for 3D Data Processing

progressive-x icon progressive-x

The Progressive-X algorithm proposed in paper: Daniel Barath and Jiri Matas; Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm, International Conference on Computer Vision, 2019. It is available at https://arxiv.org/pdf/1906.02290

pykitti icon pykitti

Python tools for working with KITTI data.

pytlsd icon pytlsd

Python transparent bindings for LSD (Line Segment Detector)

ransaclib icon ransaclib

Template-based implementation of RANSAC and its variants in C++

superpoint icon superpoint

Efficient neural feature detector and descriptor

superpointpretrainednetwork icon superpointpretrainednetwork

PyTorch pre-trained model for real-time interest point detection, description, and sparse tracking (https://arxiv.org/abs/1712.07629)

tp-lsd icon tp-lsd

Adaptation of the original "TP-LSD: Tri-points based line segment detector" with more recent torch/cuda versions

vs-net icon vs-net

VS-Net: Voting with Segmentation for Visual Localization

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