Search Results for author: Ignas Budvytis

Found 18 papers, 10 papers with code

IMP: Iterative Matching and Pose Estimation with Adaptive Pooling

1 code implementation CVPR 2023 Fei Xue, Ignas Budvytis, Roberto Cipolla

Previous methods solve feature matching and pose estimation using a two-stage process by first finding matches and then estimating the pose.

Pose Estimation

SFD2: Semantic-guided Feature Detection and Description

1 code implementation CVPR 2023 Fei Xue, Ignas Budvytis, Roberto Cipolla

Visual localization is a fundamental task for various applications including autonomous driving and robotics.

Autonomous Driving Visual Localization

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

no code implementations10 Oct 2022 Fotios Logothetis, Roberto Mecca, Ignas Budvytis, Roberto Cipolla

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered.

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

1 code implementation3 Oct 2022 Florian Langer, Gwangbin Bae, Ignas Budvytis, Roberto Cipolla

This combined information is the input to a pose prediction network, SPARC-Net which we train to predict a 9 DoF CAD model pose update.

Pose Prediction Retrieval

Efficient Large-Scale Localization by Global Instance Recognition

no code implementations CVPR 2022 Fei Xue, Ignas Budvytis, Daniel Olmeda Reino, Roberto Cipolla

Hierarchical frameworks consisting of both coarse and fine localization are often used as the standard pipeline for large-scale visual localization.

Visual Localization

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

1 code implementation CVPR 2022 Gwangbin Bae, Ignas Budvytis, Roberto Cipolla

To this end, we propose MaGNet, a novel framework for fusing single-view depth probability with multi-view geometry, to improve the accuracy, robustness and efficiency of multi-view depth estimation.

Depth Estimation

Leveraging Geometry for Shape Estimation from a Single RGB Image

1 code implementation10 Nov 2021 Florian Langer, Ignas Budvytis, Roberto Cipolla

In this work we demonstrate how cross-domain keypoint matches from an RGB image to a rendered CAD model allow for more precise object pose predictions compared to ones obtained through direct predictions.


Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

1 code implementation ICCV 2021 Gwangbin Bae, Ignas Budvytis, Roberto Cipolla

Experimental results show that the proposed method outperforms the state-of-the-art in ScanNet and NYUv2, and that the estimated uncertainty correlates well with the prediction error.

Scene Understanding Surface Normal Estimation +1

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

no code implementations27 Apr 2021 Roberto Mecca, Fotios Logothetis, Ignas Budvytis, Roberto Cipolla

In order to fill the gap in evaluating near-field photometric stereo methods, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of a varying of materials.

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

no code implementations CVPR 2021 Akash Sengupta, Ignas Budvytis, Roberto Cipolla

In contrast, we propose a new task: shape and pose estimation from a group of multiple images of a human subject, without constraints on subject pose, camera viewpoint or background conditions between images in the group.

3D Human Shape Estimation Pose Prediction

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

1 code implementation21 Sep 2020 Akash Sengupta, Ignas Budvytis, Roberto Cipolla

Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical body model) to overcome data scarcity.

3D human pose and shape estimation 3D Human Shape Estimation +3

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

no code implementations12 Sep 2020 Fotios Logothetis, Ignas Budvytis, Roberto Mecca, Roberto Cipolla

Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration.

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

no code implementations ICCV 2021 Fotios Logothetis, Ignas Budvytis, Roberto Mecca, Roberto Cipolla

We show that global physical effects can be approximated on the observation map domain and this simplifies and speeds up the data creation procedure.

Data Augmentation

Large Scale Joint Semantic Re-Localisation and Scene Understanding via Globally Unique Instance Coordinate Regression

no code implementations23 Sep 2019 Ignas Budvytis, Marvin Teichmann, Tomas Vojir, Roberto Cipolla

We obtain smaller mean distance and angular errors than state-of-the-art 6-DoF pose estimation algorithms based on direct pose regression and pose estimation from scene coordinates on all datasets.

Autonomous Driving Pose Estimation +2

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