BOP Challenge 2020 on 6D Object Localization

TL;DR

The BOP Challenge 2020 evaluated 6D object pose estimation methods using a new photorealistic dataset, showing deep learning approaches now rival traditional methods and that RGB-only training can be highly effective.

Contribution

Introduction of a large photorealistic dataset for 6D pose estimation and analysis of the effectiveness of RGB-only training and data augmentation techniques.

Findings

Deep neural network methods now match traditional point pair feature methods.

RGB-only training can achieve strong results with proper data augmentation.

Photorealistic synthetic data effectively reduces domain gap.

Abstract

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. In 2020, to reduce the domain gap between synthetic training and real test RGB images, the participants were provided 350K photorealistic training images generated by BlenderProc4BOP, a new open-source and light-weight physically-based renderer (PBR) and procedural data generator. Methods based on deep neural networks have finally caught up with methods based on point pair features, which were dominating previous editions of the challenge. Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time - out of the 26 evaluated methods, the third method was trained on RGB channels of PBR and real images, while the fifth on RGB channels of PBR images only. Strong data augmentation was identified as a key component of the top-performing CosyPose method, and the photorealism of PBR images was demonstrated effective despite the augmentation. The online evaluation system stays open and is available on the project website: bop.felk.cvut.cz.