# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 2 Related Work  
The field of 6D object pose estimation has seen significant advancements in recent years, driven by the proliferation of deep learning techniques and the increasing demand for accurate and efficient pose estimation in various application domains. Several comprehensive surveys have been conducted to synthesize the existing methodologies, datasets, and evaluation metrics. This section reviews three latest survey articles--[PERSON] et al. (2021) [1], [PERSON] et al. [2]. (2022), and [PERSON] et al. (2024) [3]-- highlighting their contributions and delineating how the present study extends beyond their scopes to offer novel insights and resources.  
[PERSON] et al. (2021) [1] present an extensive review of deep learning-based methods for 3D object detection and 6D pose estimation, with a particular emphasis on applications in autonomous vehicles. The survey meticulously categorizes existing techniques, discusses popular datasets and evaluation metrics, and identifies key challenges in the field. The authors highlight the limitations of current methods, such as the inability to handle multi-object scenarios and the challenges posed by textureless objects. Additionally, the paper underscores the necessity for real-time operation in practical applications, advocating for the development of faster and more reliable detection and pose estimation systems.  
[PERSON] et al. (2022) [2] focus their survey on 6D object detection algorithms tailored for industrial applications. The study categorizes algorithms based on their training methodologies--specifically, model-based training--and their support for RGB-D inputs. The authors provide a comprehensive analysis of both qualitative and quantitative data, evaluating various object detectors and identifying promising candidates for specific industrial challenges. A notable contribution of this survey is its emphasis on the practical applicability of algorithms in controlled industrial environments, addressing issues such as occlusion, rotational symmetry, and the handling of textureless and reflective objects. Furthermore, the paper highlights the scarcity of empirical data for many methods, advocating for standardized benchmarking to better assess algorithm suitability for industrial use cases.  
[PERSON] et al. (2024) [3] offer a thorough survey of 6D pose estimation methods, categorizing them into instance-level and category-level approaches based on the necessity of computer-aided design (CAD) models during training. This categorization facilitates a clearer understanding of applicable scenarios and underlying assumptions for each method. The survey provides detailed descriptions of widely used datasets, along with primary evaluation metrics. [PERSON] et al. also discuss ongoing challenges like occlusion handling, illumination variations, and real-time performance, proposing future research directions such as zero-shot learning and multi-view information integration.  
Building upon the foundational work of the three previous surveys, the present study extends the landscape of 6D pose estimation research as described in the introduction.