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Found 7 papers, 2 papers with code
DPOD: 6D Pose Object Detector and Refiner
An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme.
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6D Pose Estimation using RGB
on LineMOD
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects
We also present a set of benchmarks to test various desired detector properties, particularly focusing on scalability with respect to the number of objects and resistance to changing light conditions, occlusions and clutter.
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision.
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework
We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects.
Multi-View Object Pose Refinement With Differentiable Renderer
This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data.
DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation
The main conclusions is that RGB excels in correspondence estimation, while depth contributes to the pose accuracy if good 3D-3D correspondences are available.
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
