# Robust Segmentation of Partial and Imperfect Dental Arches
## 2 Related Work  
Advances in digital dentistry have significantly transformed the way dental care is delivered, enhancing precision, efficiency, and patient outcomes through cutting-edge technologies such as CAD/CAM systems and artificial intelligence (AI). Utilization of CAD/CAM technology offers enhanced precision and reproducibility, leading to better patient satisfaction and reduced clinical time [10; 11; 12; 13]. Advanced digital imaging techniques have enhanced the accuracy and efficiency of assessing occlusion, which are crucial for effective treatment planning [14]. AI is also revolutionizing digital dentistry by making procedures faster and more personalized, offering enhanced diagnostics, automation, and continual learning [15; 16]. Advanced diagnostics, precise prosthetic design, and accurate treatment planning demand automated and precise identification and analysis of individual teeth in medical imaging, such as X-rays, CT scans, and 3D intraoral scans. Therefore, accurately identifying and separating teeth from the surrounding tissue through semantic segmentation is crucial. Various methodologies like those introduced in [3; 8] have advanced this goal, albeit with limitations in handling dental irregularities and anomalies. Techniques such as DentalMAE [17] and DBGANet [18] further contribute to this domain by offering advanced frameworks for 3D dental model segmentation. Nevertheless, these methods grapple with challenges in broader clinical adoption and performance in specific conditions. CNNs [19] and deep learning advancements [20] signify major strides forward in addressing root anatomy and artifacts in CBCT images [21; 22]. Yet, they encounter obstacles in data annotation and model generalization, highlighting the ongoing need for innovative solutions. In response to these barriers, the field saw the integration of novel methods by the authors of [23] and the development of a segmentation and labeling framework in [24]. Each was designed to enhance model accuracy further and handle dental abnormalities more effectively. Similarly, a recent work on utilizing a single 3D intraoral scan for segmentation opened new pathways for self-supervision methods in digital orthodontics, directly tackling the issue of manual labeling and data scarcity [25]. The evaluation of AI-generated tooth models in [26] aimed at overcoming the challenge of producing accurate tooth models for clinical scenarios with mild crowding and without dental restorations. This exploration into AI-driven methodologies highlighted the potential for increased efficiency and effectiveness, yet also emphasizing the necessity for research on complex scenarios. Nevertheless, segmentation on non-Euclidean data through MeshSegNet [7] and PointNet [27] received a substantial amount of attention by the digital dentistry community. These methods were developed in direct response to the limitations of traditional voxelization techniques, offering innovative solutions to accurately model the complex geometry of teeth and gums. However, due to the huge variability and complexity of dental data, these methods also faced the challenge of accurately processing dental models with missing teeth, partial arches, and dental preparations. Subsequent advancements pushed the boundaries of accuracy and efficiency [28; 29; 30]. While showcasing the potential for handling complex dental anomalies, these methods also faced challenges in processing dental models with extreme abnormalities or incomplete scans, emphasizing the crucial need for enhanced model generalization on imperfect arches. The advent of transformer-based methods, illustrated in [31; 32; 33; 34; 35], highlight a significant shift towards using transformer architectures for understanding 3D point clouds. With innovations such as group vector attention and partition-based pooling methods, these models offer a substantial leap in capturing the intricate patterns within dental point clouds.