Search Results for author: Frank Michel

Found 8 papers, 3 papers with code

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

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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