BOP: Benchmark for 6D Object Pose Estimation
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical...
Saved in:
Main Authors | , , , , , , , , , , , , , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
24.08.2018
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | We propose a benchmark for 6D pose estimation of a rigid object from a single
RGB-D input image. The training data consists of a texture-mapped 3D object
model or images of the object in known 6D poses. The benchmark comprises of: i)
eight datasets in a unified format that cover different practical scenarios,
including two new datasets focusing on varying lighting conditions, ii) an
evaluation methodology with a pose-error function that deals with pose
ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that
captures the status quo of the field, and iv) an online evaluation system that
is open for continuous submission of new results. The evaluation shows that
methods based on point-pair features currently perform best, outperforming
template matching methods, learning-based methods and methods based on 3D local
features. The project website is available at bop.felk.cvut.cz. |
---|---|
DOI: | 10.48550/arxiv.1808.08319 |