BOP Challenge 2020 on 6D Object Localization

By Tomas Hodan, Martin Sundermeyer, Bertram Drost, Yann Labb'e, Eric Brachmann, Frank Michel, Carsten Rother, Jiri Matas. ECCV Workshops, 2020

331 citations recorded by the source corpus.

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: this http URL.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/6c7ac8563ddd0582f7353add31b8bac52008a577

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Citations in the corpus

GDRNPP: A Geometry-Guided and Fully Learning-Based Object Pose Estimator (2021, unknown): “Please refer to [104] for a detailed explanation of these metrics.”
DexYCB: A Benchmark for Capturing Hand Grasping of Objects (2021, methodology): “Many datasets [48, 49, 16, 17] have been introduced in both domains and have facilitated progress on these two problems in parallel.”
Unknown Object Segmentation from Stereo Images (2021, methodology): “As an upper performance bound we train a MaskRCNN directly on photorealistically rendered 3D models of the YCB-V dataset used in the BOP Challenge [70].”