Object Coordinate Space

Object coordinate space (OCS) research aims to represent 3D object locations and shapes in a consistent and generalizable manner, primarily focusing on methods to accurately reconstruct 3D objects from 2D images or other limited sensory data. Current research emphasizes learning-based approaches, often employing transformer networks or other deep learning architectures, to predict normalized object coordinate spaces (NOCS) and improve 3D object pose estimation, particularly for challenging scenarios like unseen object classes or transparent objects. This work is significant for advancing computer vision and robotics applications, enabling more robust and efficient object manipulation, scene understanding, and autonomous navigation.