Object Pose

Object pose estimation aims to determine an object's 3D position and orientation relative to a chosen coordinate system, a crucial task for robotics, augmented reality, and other applications. Current research emphasizes improving accuracy and robustness, particularly in challenging scenarios like occlusion, symmetry, and limited data, often employing deep learning architectures like convolutional neural networks and diffusion models, along with techniques such as pose graph optimization and equivariant representations. These advancements are driving progress in areas such as robotic manipulation, scene understanding, and human-computer interaction, enabling more sophisticated and reliable interactions between machines and the physical world.