5 accepted papers
This paper introduces EA6D, a novel diffusion-based framework for 6D pose estimation that operates effectively in any environment. Traditional pose estimation methods struggle with the variability and complexity of real-world scenarios, often leading to overfitting on controlled datasets and poor ge…
Accurate human motion prediction is vital for diverse artificial intelligence applications, and recent research has yielded substantial advancements. Despite this, the prediction process often encounters abrupt discontinuities and accumulates errors over the long term due to insufficient modeling of…
Many recent 6D pose estimation methods exploited object 3D models to generate synthetic images for training because labels come for free. However, due to the domain shift of data distributions between real images and synthetic images, the network trained only on synthetic images fails to capture rob…
The state-of-art 6D object pose detection methods use convolutional neural networks to estimate objects' 6D poses from RGB images. However, they require huge numbers of images with explicit 3D annotations such as 6D poses, 3D bounding boxes and 3D keypoints, either obtained by manual labeling or inf…
Human-multi-robot collaboration is becoming more and more common in intelligent manufacturing. Optimal assembly scheduling of such systems plays a critical role in their production efficiency. Existing approaches mostly consider humans as agents with assumed or known capabilities, which leads to sub…