Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
Source abstract
Abstract
Three-dimensional object recognition is crucial in modern applications, including robotics in manufacturing, household items, augmented and virtual reality, and autonomous driving. Extensive research and numerous surveys have been conducted in this field. This study aims to create a model selection guide by addressing key questions we need to answer when we want to select a 6D pose estimation model: inputs, modalities, real-time capabilities, hardware requirements, evaluation datasets, performance metrics, strengths, limitations, and special attributes such as symmetry or occlusion handling. By analyzing 84 models, including 62 new ones beyond previous surveys, and identifying 25 datasets 14 newly introduced, we organized the results into comparison tables and standardized summarization templates. This structured approach facilitates easy model comparison and selection based on practical application needs. The focus of this study is on the practical aspects of utilizing 6D pose estimation models, providing a valuable resource for researchers and practitioners.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Robotics and Sensor-Based Localization · Robotic Mechanisms and Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilini, Greece
MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece
References (100+)
Show source-provided references
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