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Found 10 papers, 1 papers with code
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.
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.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
BOP: Benchmark for 6D Object Pose Estimation
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image.
Category-level 6D Object Pose Recovery in Depth Images
Intra-class variations, distribution shifts among source and target domains are the major challenges of category-level tasks.
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.
Recovering 6D Object Pose: A Review and Multi-modal Analysis
A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality.
A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth
The iterative refinement is accomplished based on finer (smaller) parts that are represented with more discriminative control point descriptors by using our Iterative Hough Forest.
Iterative Hough Forest with Histogram of Control Points for 6 DoF Object Registration from Depth Images
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space.
