6D Pose Estimation using RGBD
23 papers with code • 7 benchmarks • 4 datasets
Image: Zeng et al
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LibrariesUse these libraries to find 6D Pose Estimation using RGBD models and implementations
Most implemented papers
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources.
BOP Challenge 2020 on 6D Object Localization
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image.
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation.
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
The approach was part of the MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and picking tasks, respectively at APC 2016.
6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
We present 6-PACK, a deep learning approach to category-level 6D object pose tracking on RGB-D data.
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image.
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.