Transparent object perception is a rapidly develo** research problem in artificial
intelligence. The ability to perceive transparent objects enables robots to achieve higher …

We present the evaluation methodology datasets and results of the BOP Challenge 2023 the
fifth in a series of public com-petitions organized to capture the state of the art in model …

We present the evaluation methodology, datasets and results of the BOP Challenge 2022,
the fourth in a series of public competitions organized with the goal to capture the status quo …

BlenderProc2 is a procedural pipeline that can render realistic images for the training of
neural networks. Our pipeline can be employed in various use cases, including …

Objective: Deep learning-based object recognition, 6D pose estimation, and semantic scene
understanding require a large amount of training data to achieve generalization. Time …

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 …

The past decade has witnessed the rapid development of autonomous driving systems.
However, it remains a daunting task to achieve full autonomy, especially when it comes to …

We propose FoundPose, a model-based method for 6D pose estimation of unseen objects
from a single RGB image. The method can quickly onboard new objects using their 3D …

In this work, we introduce CC3D, a conditional generative model that synthesizes complex
3D scenes conditioned on 2D semantic scene layouts, trained using single-view images …

It is difficult to precisely annotate object instances and their semantics in 3D space, and as
such, synthetic data are extensively used for these tasks, eg, category-level 6D object pose …