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Found 6 papers, 0 papers with code
Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images
Most successful approaches to estimate the 6D pose of an object typically train a neural network by supervising the learning with annotated poses in real world images.
A Review on Object Pose Recovery: from 3D Bounding Box Detectors to Full 6D Pose Estimators
In this paper, we present the first comprehensive and most recent review of the methods on object pose recovery, from 3D bounding box detectors to full 6D pose estimators.
Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning
In this work, we explore how a strategic selection of camera movements can facilitate the task of 6D multi-object pose estimation in cluttered scenarios while respecting real-world constraints important in robotics and augmented reality applications, such as time and distance traveled.
Instance- and Category-level 6D Object Pose Estimation
6D object pose estimation is an important task that determines the 3D position and 3D rotation of an object in camera-centred coordinates.
Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios
Our architecture jointly learns multiple sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects; and joint registration of multiple objects.
Pose Guided RGBD Feature Learning for 3D Object Pose Estimation
In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation.
