In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …

One critical challenge in 6D object pose estimation from a single RGBD image is efficient
integration of two different modalities, ie, color and depth. In this work, we tackle this problem …

For robots to be useful outside labs and specialized factories we need a way to teach them
new useful behaviors quickly. Current approaches lack either the generality to onboard new …

In this paper, we introduce neural texture learning for 6D object pose estimation from
synthetic data and a few unlabelled real images. Our major contribution is a novel learning …

Most self-supervised 6D object pose estimation methods can only work with additional depth
information or rely on the accurate annotation of 2D segmentation masks, limiting their …

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 …

Multi-instance point cloud registration estimates the poses of multiple instances of a model
point cloud in a scene point cloud. Extracting accurate point correspondences is to the …

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

The task of fully automated picking of novel bin objects that are placed in a densely cluttered
pile poses a significant challenge. It becomes even more challenging if the objects are of …

The use of deep learning (DL) algorithms has improved the performance of vision‐based
space applications in recent years. However, generating large amounts of annotated data …