6D Pose Estimation using RGB
86 papers with code • 6 benchmarks • 6 datasets
6D Pose Estimation using RGB refers to the task of determining the six degree-of-freedom (6D) pose of an object in 3D space based on RGB images. This involves estimating the position and orientation of an object in a scene, and is a fundamental problem in computer vision and robotics. In this task, the goal is to estimate the 6D pose of an object given an RGB image of the object and the scene, which can be used for tasks such as robotic manipulation, augmented reality, and scene reconstruction.
( Image credit: Segmentation-driven 6D Object Pose Estimation )
Benchmarks
These leaderboards are used to track progress in 6D Pose Estimation using RGB
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Libraries
Use these libraries to find 6D Pose Estimation using RGB models and implementations
YCB-Video
T-LESS
LM
ApolloCar3D
Fraunhofer IPA Bin-Picking
Drunkard's Dataset
Latest papers with no code
GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects
Despite the progress of learning-based methods for 6D object pose estimation, the trade-off between accuracy and scalability for novel objects still exists.
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data
In this paper, we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data.
GS-Pose: Cascaded Framework for Generalizable Segmentation-based 6D Object Pose Estimation
At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial 6D pose using a retrieval approach, and refining the pose with a render-and-compare method.
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, 1 GPU) from provided 3D object models.
Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation
In this work, we propose a method to quantify the uncertainty of multi-stage 6D object pose estimation approaches with deep ensembles.
Improving 2D-3D Dense Correspondences with Diffusion Models for 6D Object Pose Estimation
In this study, we compare image-to-image translation networks based on GANs and diffusion models for the downstream task of 6D object pose estimation.
Extending 6D Object Pose Estimators for Stereo Vision
Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task.
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds.
Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation
This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs).
Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment
This paper focuses on synthetic data generation procedures for parts and assemblies used in a production environment.
