BOP: Benchmark for 6D Object Pose Estimation

Abstract

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.

ECCV 2018

References in corpus (3)

• Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition
• Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
• Global Hypothesis Generation for 6D Object Pose Estimation

Cited by in corpus (8)

• DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation
• CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
• Semantic keypoint-based pose estimation from single RGB frames
• Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning
• Zero in on Shape: A Generic 2D-3D Instance Similarity Metric learned from Synthetic Data
• Category-level 6D Object Pose Recovery in Depth Images
• Single-view robot pose and joint angle estimation via render & compare
• Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter