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 …

In this work we present a novel dense-correspondence method for 6DoF object pose
estimation from a single RGB-D image. While many existing data-driven methods achieve …

Recent learning methods for object pose estimation require resource-intensive training for
each individual object instance or category hampering their scalability in real applications …

Estimating the 6D pose of objects from RGB-D images is a fundamental problem in
computer vision, with the primary challenge lying in effectively fusing these two modalities of …

Embodied learning for object-centric robotic manipulation is a rapidly developing and
challenging area in embodied AI. It is crucial for advancing next-generation intelligent robots …

Object pose refinement is essential for robust object pose estimation. Previous work has
made significant progress towards instance-level object pose refinement. Yet category-level …

Object recognition and pose estimation are critical components in autonomous robot
manipulation systems, playing a crucial role in enabling robots to interact effectively with the …

In this work we introduce a novel method for calculating the 6DoF pose of an object using a
single RGB-D image. Unlike existing methods that either directly predict objects' poses or …

Abstract 6D pose estimation plays a crucial role in enabling intelligent robots to interact with
their environment by understanding 3D scene information. This task is challenging due to …

The availability of affordable RGB-D sensors has made it more suitable to use RGB-D
images for accurate 6D pose estimation, which allows for precise 6D parameter prediction …