ReviewSensors (Basel, Switzerland)2024
A Survey of 6DoF Object Pose Estimation Methods for Different Application Scenarios.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- RAFusion: Integrating Residual Attention for Global Context-Aware 6D Pose Estimation.Sensors (Basel, Switzerland) · 2026Article
- The ADePT framework for assessing autonomous laboratory robotics.Communications chemistry · 2026Review
- Enhanced RGB-D feature extraction for 6D pose estimation.Scientific reports · 2026Article
- MOTT modular optical tool tracking framework enabling efficient benchmarking.Scientific reports · 2025Article
- An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items.Sensors (Basel, Switzerland) · 2025Article
- Vision-Based 6D Pose Analytics Solution for High-Precision Industrial Robot Pick-and-Place Applications.Sensors (Basel, Switzerland) · 2025Article
- Enhancing object pose estimation for RGB images in cluttered scenes.Scientific reports · 2025Article
- A Survey of Robotic Monocular Pose Estimation.Sensors (Basel, Switzerland) · 2025Review
- E-POSE: A Large Scale Event Camera Dataset for Object Pose Estimation.Scientific data · 2025Article
- GFA-Net: Geometry-Focused Attention Network for Six Degrees of Freedom Object Pose Estimation.Sensors (Basel, Switzerland) · 2024Article
- EBFA-6D: End-to-End Transparent Object 6D Pose Estimation Based on a Boundary Feature Augmented Mechanism.Sensors (Basel, Switzerland) · 2024Article
- Towards Automatic Object Detection and Activity Recognition in Indoor Climbing.Sensors (Basel, Switzerland) · 2024Article
- Large-Scale Indoor Camera Positioning Using Fiducial Markers.Sensors (Basel, Switzerland) · 2024Article
- A fast monocular 6D pose estimation method for textureless objects based on perceptual hashing and template matching.Frontiers in robotics and AI · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, 6DoF object pose estimation has become increasingly important for a broad range of applications in the fields of virtual reality, augmented reality, autonomous driving, and robotic operations. This task involves extracting the target area from the input data and subsequently determining the position and orientation of the objects. In recent years, many new advances have been made in pose estimation. However, existing reviews have the problem of only summarizing category-level or instance-level methods, and not comprehensively summarizing deep learning methods. This paper will provide a comprehensive review of the latest progress in 6D pose estimation to help researchers better understanding this area. In this study, the current methods about 6DoF object pose estimation are mainly categorized into two groups: instance-level and category-level groups, based on whether it is necessary to acquire the CAD model of the object. Recent advancements about learning-based 6DoF pose estimation methods are comprehensively reviewed. The study systematically explores the innovations and applicable scenarios of various methods. It provides an overview of widely used datasets, task metrics, and diverse application scenarios. Furthermore, state-of-the-art methods are compared across publicly accessible datasets, taking into account differences in input data types. Finally, we summarize the challenges of current tasks, methods for different applications, and future development directions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.