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.

To address these issues and facilitate progress in 6D object pose estimation, this paper introduces Omni6DPose, a substantial dataset featured by its diversity in object categories, large scale, and variety in object materials.

We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured.

Mar 7, 2025 · To address these issues, we propose a Single-Reference-based novel object 6D (SinRef-6D) pose estimation method. Our key idea is to iteratively establish point-wise alignment in the camera coordinate system based on state space models (SSMs).

Dec 13, 2023 · We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured.

Nov 4, 2022 · In literature, 6D object pose estimation algorithms from a single RGB image can be divided into three main categories using traditional image processing techniques or deep learning: These...

1 day ago · This paper tackles category-level pose estimation of articulated objects in robotic manipulation tasks and introduces a new benchmark dataset. While recent methods estimate part poses and sizes at the category level, they often rely on geometric cues and complex multi-stage pipelines that first segment parts from the point cloud, followed by Normalized Part Coordinate Space (NPCS) estimation ...

This paper briefly describes the 6D object pose estimation technology, introduces various traditional 6D object pose estimation methods, summarizes and analyzes the 6D object pose estimation algorithms based on deep learning and the data sets.

This paper provides a comprehensive review of traditional 6D pose estimation methods, deep learning approaches, and point cloud techniques by analyzing their advantages and disadvantages. It also discusses evaluation metrics and performance on common datasets for 6D pose estimation.

We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured.