Search Results for author: Eric Brachmann

Found 20 papers, 12 papers with code

BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects

no code implementations25 Feb 2023 Martin Sundermeyer, Tomas Hodan, Yann Labbe, Gu Wang, Eric Brachmann, Bertram Drost, Carsten Rother, Jiri Matas

In 2022, we witnessed another significant improvement in the pose estimation accuracy -- the state of the art, which was 56. 9 AR$_C$ in 2019 (Vidal et al.) and 69. 8 AR$_C$ in 2020 (CosyPose), moved to new heights of 83. 7 AR$_C$ (GDRNPP).

2D object detection 6D Pose Estimation using RGB +2

On the Limits of Pseudo Ground Truth in Visual Camera Re-localisation

1 code implementation ICCV 2021 Eric Brachmann, Martin Humenberger, Carsten Rother, Torsten Sattler

This begs the question whether the choice of the reference algorithm favours a certain family of re-localisation methods.

Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision

1 code implementation6 Apr 2021 Mehmet Ozgur Turkoglu, Eric Brachmann, Konrad Schindler, Gabriel Brostow, Aron Monszpart

Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment.


BOP Challenge 2020 on 6D Object Localization

4 code implementations15 Sep 2020 Tomas Hodan, Martin Sundermeyer, Bertram Drost, Yann Labbe, Eric Brachmann, Frank Michel, Carsten Rother, Jiri Matas

This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image.

6D Pose Estimation 6D Pose Estimation using RGB +3

Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC

no code implementations27 Feb 2020 Eric Brachmann, Carsten Rother

The framework consists of a deep neural network and fully differentiable pose optimization.

Visual Localization

Expert Sample Consensus Applied to Camera Re-Localization

1 code implementation ICCV 2019 Eric Brachmann, Carsten Rother

In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment.

Camera Localization Visual Localization

Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses

3 code implementations ICCV 2019 Eric Brachmann, Carsten Rother

In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks.

Camera Localization Horizon Line Estimation +1

iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects

no code implementations5 Dec 2017 Omid Hosseini Jafari, Siva Karthik Mustikovela, Karl Pertsch, Eric Brachmann, Carsten Rother

We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.

6D Pose Estimation 6D Pose Estimation using RGB +2

Learning Less is More - 6D Camera Localization via 3D Surface Regression

1 code implementation CVPR 2018 Eric Brachmann, Carsten Rother

Popular research areas like autonomous driving and augmented reality have renewed the interest in image-based camera localization.

Camera Localization regression +1

Global Hypothesis Generation for 6D Object Pose Estimation

no code implementations CVPR 2017 Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother

Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.

6D Pose Estimation using RGB

DSAC - Differentiable RANSAC for Camera Localization

4 code implementations CVPR 2017 Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother

The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.

Camera Localization Visual Localization

Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image

no code implementations CVPR 2016 Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother

In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.

6D Pose Estimation 6D Pose Estimation using RGB +1

Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

no code implementations ICCV 2015 Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother

This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.

6D Pose Estimation 6D Pose Estimation using RGB

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