6D Pose Estimation using RGB

85 papers with code • 6 benchmarks • 5 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 )

Libraries

Use these libraries to find 6D Pose Estimation using RGB models and implementations

YOLOv5-6D: Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries

cviviers/YOLOv5-6D-Pose IEEE Transactions on Image Processing 2024

We propose a general-purpose approach of data acquisition for 6-DoF pose estimation tasks in X-ray systems, a novel and general purpose YOLOv5-6D pose architecture for accurate and fast object pose estimation and a complete method for surgical screw pose estimation under acquisition geometry consideration from a monocular cone-beam X-ray image.

7
22 Mar 2024

Real-time Holistic Robot Pose Estimation with Unknown States

Oliverbansk/Hollistic-Robot-Pose-Estimation 8 Feb 2024

We propose an end-to-end pipeline for real-time, holistic robot pose estimation from a single RGB image, even in the absence of known robot states.

7
08 Feb 2024

FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

NVlabs/FoundationPose 13 Dec 2023

We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups.

385
13 Dec 2023

SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation

jiehonglin/sam-6d 27 Nov 2023

Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes, presenting significant challenges for model generalizability.

158
27 Nov 2023

Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors

hsp-iit/sim2real-surface-classification 2 Nov 2023

Our evaluation is conducted on a dataset of tactile images obtained from a set of ten 3D printed YCB objects.

0
02 Nov 2023

SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

jiang-hb/diffusionreg NeurIPS 2023

By contrast, the SE(3) reverse process focuses on learning a denoising network that refines the noisy transformation step-by-step, bringing it closer to the optimal transformation for accurate pose estimation.

44
26 Oct 2023

DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation

zray26/dr-pose 5 Sep 2023

In the second stage, a novel registration network is designed to extract pose-sensitive features and predict the representation of object partial point cloud in canonical space based on the deformation results from the first stage.

8
05 Sep 2023

VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations

jiehonglin/vi-net ICCV 2023

We apply the proposed VI-Net to the challenging task of category-level 6D object pose estimation for predicting the poses of unknown objects without available CAD models; experiments on the benchmarking datasets confirm the efficacy of our method, which outperforms the existing ones with a large margin in the regime of high precision.

36
19 Aug 2023

Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation

yanghai-1218/pseudoflow ICCV 2023

Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their application range.

26
19 Aug 2023

Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation

junzastar/dftr_voting ICCV 2023

One critical challenge in 6D object pose estimation from a single RGBD image is efficient integration of two different modalities, i. e., color and depth.

21
10 Aug 2023