Ignas Budvytis

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

A CNN Based Approach for the Point-Light Photometric Stereo Problem.

[BibT_eX]

[DOI]

Int. J. Comput. Vis., 2023

Function-constrained Program Synthesis.

[BibT_eX]

[DOI]

Patrick Hajali

CoRR, 2023

IMP: Iterative Matching and Pose Estimation with Adaptive Pooling.

[BibT_eX]

[DOI]

Fei Xue

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

SFD2: Semantic-Guided Feature Detection and Description.

[BibT_eX]

[DOI]

Fei Xue

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Sparse Multi-Object Render-and-Compare.

[BibT_eX]

[DOI]

Florian Langer

Proceedings of the 34th British Machine Vision Conference 2023, 2023

2022

Efficient Large-scale Localization by Global Instance Recognition.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry.

[BibT_eX]

[DOI]

Gwangbin Bae

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

SPARC: Sparse Render-and-Compare for CAD model alignment in a single RGB Image.

[BibT_eX]

[DOI]

Proceedings of the 33rd British Machine Vision Conference 2022, 2022

IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty.

[BibT_eX]

[DOI]

Gwangbin Bae

Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021

Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation.

[BibT_eX]

[DOI]

Gwangbin Bae

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Probabilistic 3D Human Shape and Pose Estimation From Multiple Unconstrained Images in the Wild.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Probabilistic Estimation of 3D Human Shape and Pose with a Semantic Local Parametric Model.

[BibT_eX]

[DOI]

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo.

[BibT_eX]

[DOI]

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

Leveraging Geometry for Shape Estimation from a Single RGB Image.

[BibT_eX]

[DOI]

Florian Langer

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

Lifted Semantic Graph Embedding for Omnidirectional Place Recognition.

[BibT_eX]

[DOI]

Proceedings of the International Conference on 3D Vision, 2021

2020

PX-NET: Simple, Efficient Pixel-Wise Training of Photometric Stereo Networks.

[BibT_eX]

[DOI]

CoRR, 2020

Deep Multi-view Stereo for Dense 3D Reconstruction from Monocular Endoscopic Video.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild.

[BibT_eX]

[DOI]

Proceedings of the 31st British Machine Vision Conference 2020, 2020

A CNN Based Approach for the Near-Field Photometric Stereo Problem.

[BibT_eX]

[DOI]

Proceedings of the 31st British Machine Vision Conference 2020, 2020

Rotation Equivariant Orientation Estimation for Omnidirectional Localization.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

Efficient Large-Scale Semantic Visual Localization in 2D Maps.

[BibT_eX]

[DOI]

Tomas Vojir

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

2019

Large scale joint semantic re-localisation and scene understanding via globally unique instance coordinate regression.

[BibT_eX]

[DOI]

Proceedings of the 30th British Machine Vision Conference 2019, 2019

2018

Semantic Localisation via Globally Unique Instance Segmentation.

[BibT_eX]

[DOI]

Patrick Sauer

Proceedings of the British Machine Vision Conference 2018, 2018

2017

Large Scale Labelled Video Data Augmentation for Semantic Segmentation in Driving Scenarios.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

Indirect deep structured learning for 3D human body shape and pose prediction.

[BibT_eX]

[DOI]

Vince Tan

Proceedings of the British Machine Vision Conference 2017, 2017

Real-time Factored ConvNets: Extracting the X Factor in Human Parsing.

[BibT_eX]

[DOI]

James Charles

Proceedings of the British Machine Vision Conference 2017, 2017

2014

Mixture of Trees Probabilistic Graphical Model for Video Segmentation.

[BibT_eX]

[DOI]

Int. J. Comput. Vis., 2014

2013

Semi-Supervised Video Segmentation Using Tree Structured Graphical Models.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2013

2012

Making a Shallow Network Deep: Conversion of a Boosting Classifier into a Decision Tree by Boolean Optimisation.

[BibT_eX]

[DOI]

Tae-Kyun Kim

Int. J. Comput. Vis., 2012

MoT - Mixture of Trees Probabilistic Graphical Model for Video Segmentation.

[BibT_eX]

[DOI]

Proceedings of the British Machine Vision Conference, 2012

2010

Making a Shallow Network Deep: Growing a Tree from Decision Regions of a Boosting Classifier.

[BibT_eX]

[DOI]

Tae-Kyun Kim

Proceedings of the British Machine Vision Conference, 2010

Label propagation in complex video sequences using semi-supervised learning.

[BibT_eX]

[DOI]