DPOD | Object Pose Estimation

Object Pose Estimation · 2019 · ICCV · CCF A

DPOD: 6D Pose Object Detector and Refiner

Predicts multiclass dense correspondence maps, then uses PnP/RANSAC and RGB refinement to recover multiple object 6D poses. It is a representative RGB dense-correspondence approach.

Paper

Research area

Object Pose Estimation

Task level

Instance-level

Object setting

Seen objects

Input modalities

RGB

Requirements

camera intrinsicsCAD modeldense 2D-3D correspondencesrenderer for refinement

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

Authors: Sergey Zakharov, Ivan Shugurov, Slobodan Ilic
Affiliations: Technical University of Munich / Siemens
Conference / journal: Conference · ICCV · CCF A
Research area: Object Pose Estimation
Code availability: Unavailable / not yet recorded
Reading status: Overview:Pending Deep Dive:Pending
Task setting: Instance-level · Seen objects
Datasets: LINEMOD Occlusion LINEMOD
Evaluation metrics: ADD ADD-S 2D Projection
Method tags: 2D–3D Correspondences Dense Correspondences & Coordinate Fields Pose Refinement Multi-object Pose Estimation Instance-level Pose Estimation Absolute Pose Estimation
Requirements: camera intrinsicsCAD modeldense 2D-3D correspondencesrenderer for refinement

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