Projects:

Object pose estimation is crucial for robotic applications and augmented reality. To provide a benchmark with high-quality ground truth annotations to the community, we introduce a multimodal dataset for category-level object pose estimation with photometrically challenging objects termed PhoCaL. PhoCaL comprises 60 high quality 3D models of household objects over 8 categories including highly reflective, transparent and symmetric objects. We developed a novel robot-supported multi-modal (RGB, depth, polarisation) data acquisition and annotation process. It ensures sub-millimeter accuracy of the pose for opaque textured, shiny and transparent objects, no motion blur and perfect camera synchronisation. PhoCAL Dataset Download [Link]

Current 6D pose estimation networks support only a limited number of objects and require much training with either synthetic or real annotations, which is not suitable for robotic grasping in changing indoor environments. Therefore, we propose a method to estimate object shape along with grasp points on the fly with a sequence of RGB-D images. The learned knowledge is then leveraged to guide the robot to detect and grasp the object in the real environment. We conducted exhaustive experiments in both synthetic and real environments and our method outperforms SOTA grasping networks.