论文标题
BOP挑战2020上的6D对象本地化
BOP Challenge 2020 on 6D Object Localization
论文作者
论文摘要
本文介绍了BOP Challenge 2020的评估方法，数据集和结果，这是一系列公共竞赛中的第三次，其目标是从RGB-D图像中捕获6D对象姿势估算领域的现状。在2020年，为了减少合成训练和实际测试RGB图像之间的域间隙，为参与者提供了由BlenderProc4Bop生成的350K逼真的训练图像，BlenderProc4Bop是一种新的开源和轻巧的基于物理的渲染器（PBR）和程序数据生成器。基于深度神经网络的方法终于抓住了基于点对特征的方法，这些方法主要主导了以前的挑战版本。尽管表现最佳的方法依赖于RGB-D图像通道，但是在训练和测试时间仅使用RGB通道（在26种评估的方法中），在RGB的RGB通道和真实图像中培训了第三种方法，而在PBR图像的RGB通道上仅使用了第五个方法，则取得了强烈的结果。强大的数据增强被确定为表现最佳的综合方法的关键组成部分，尽管增强了PBR图像的光真实性证明有效。在线评估系统保持开放，可在项目网站上找到：bop.felk.cvut.cz.
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.
