Pinned Repositories
consac
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
cuboids_revisited
Robust Shape Fitting for 3D Scene Abstraction
hope-f
HOPE-F Dataset for Multiple Fundamental Matrix Fitting
nyu_vp
NYU-VP: Vanishing Point Labels for NYUv2
parsac
PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus
smh
Synthetic Metropolis Homography Dataset for Multiple Homography Fitting
tnt-battlesnake
vanishing_points_2017
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
vp-linkage
Implementation of J-Linkage and T-Linkage for vanishing point estimation.
yud_plus
YUD+: Additional Vanishing Point Labels for YUD
fkluger's Repositories
fkluger/consac
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
fkluger/cuboids_revisited
Robust Shape Fitting for 3D Scene Abstraction
fkluger/vanishing_points_2017
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
fkluger/parsac
PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus
fkluger/vp-linkage
Implementation of J-Linkage and T-Linkage for vanishing point estimation.
fkluger/nyu_vp
NYU-VP: Vanishing Point Labels for NYUv2
fkluger/tnt-battlesnake
fkluger/yud_plus
YUD+: Additional Vanishing Point Labels for YUD
fkluger/tchl
Temporally Consistent Horizon Lines
fkluger/kitti_horizon
KITTI dataset horizon line extension proposed in the paper "Temporally Consistent Horizon Lines"
fkluger/bicycle_detection
fkluger/cuboids_revisited_cvpr21
fkluger/backintime
Back In Time - A simple backup tool for Linux
fkluger/hope-f
HOPE-F Dataset for Multiple Fundamental Matrix Fitting
fkluger/smh
Synthetic Metropolis Homography Dataset for Multiple Homography Fitting
fkluger/bts
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
fkluger/cvpr20_LESA
fkluger/fkluger.github.io
fkluger/lsd-python
Python wrapper for LSD line segment detector
fkluger/monodepth2
Monocular depth estimation from a single image
fkluger/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
fkluger/road_damage_2018
fkluger/simple_vp_calibration
fkluger/superquadric_parsing
Code for "Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids", CVPR 2019
fkluger/torchgeometry
TGM: PyTorch Geometry
fkluger/volumetricPrimitives
Code release for "Learning Shape Abstractions by Assembling Volumetric Primitives " (CVPR 2017)