EPOS | Object Pose Estimation

Object Pose Estimation · 2020 · CVPR · CCF A

EPOS: Estimating 6D Pose of Objects with Symmetries

Represents symmetric objects with surface fragments, predicting per-pixel object probabilities, fragment probabilities, and 3D positions before running PnP-RANSAC for potentially multiple instances.

Paper Code

Research area

Object Pose Estimation

Task level

Instance-level

Object setting

Seen objects

Input modalities

RGB

Requirements

camera intrinsicsCAD modelobject fragments

Full paper metadata Authors, affiliations, research area, datasets, metrics, method tags, and reading status Expand

Authors: Tomas Hodan, Daniel Barath, Jiri Matas
Affiliations: Czech Technical University in Prague
Conference / journal: Conference · CVPR · CCF A
Research area: Object Pose Estimation
Code availability: Code available
Reading status: Overview:Pending Deep Dive:Pending
Task setting: Instance-level · Seen objects
Datasets: T-LESS Occlusion LINEMOD YCB-Video BOP
Evaluation metrics: VSD MSSD MSPD BOP AR
Method tags: 2D–3D Correspondences Dense Correspondences & Coordinate Fields Multi-object Pose Estimation Instance-level Pose Estimation Absolute Pose Estimation
Requirements: camera intrinsicsCAD modelobject fragments

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