Recently, 6DoF object pose estimation has become increasingly important for a broad range
of applications in the fields of virtual reality, augmented reality, autonomous driving, and …

Rotation estimation of high precision from an RGB-D object observation is a huge challenge
in 6D object pose estimation, due to the difficulty of learning in the non-linear space of SO …

Reconstructing hand-held objects from a single RGB image is an important and challenging
problem. Existing works utilizing Signed Distance Fields (SDF) reveal limitations in …

Category-level 6DoF object pose estimation intends to estimate the rotation, translation, and
size of unseen objects. Many previous works use point clouds as a pre-learned shape prior …

Monocular 3D object detection has recently made a significant leap forward thanks to the
use of pre-trained depth estimators for pseudo-LiDAR recovery. Yet, such two-stage …

Previous works concerning single-view hand-held object reconstruction typically rely on
supervision from 3D ground-truth models which are hard to collect in real world. In contrast …

Robotic manipulation requires accurate perception of the environment, which poses a
significant challenge due to its inherent complexity and constantly changing nature. In this …

Category-level 6D object pose estimation aims to estimate the rotation translation and size
of unseen instances within specific categories. In this area dense correspondence-based …

Category-level 6D object pose estimation aims at determining the pose of an object of a
given category. Most current state-of-the-art methods require a significant amount of real …

Most existing methods for category-level pose estimation rely on object point clouds.
However, when considering transparent objects, depth cameras are usually not able to …