This paper presents PCDreamer, a novel method for point cloud completion. Traditional methods typically extract features from partial point clouds to predict missing regions, but the large solution space often leads to unsatisfactory results. More recent approaches have started to use images as extra guidance, effectively improving performance, but obtaining paired data of images and partial point clouds is challenging in practice. To overcome these limitations, we harness the relatively view-consistent multi-view diffusion priors within large models, to generate novel views of the desired shape. The resulting image set encodes both global and local shape cues, which are especially beneficial for shape completion. To fully exploit the priors, we have designed a shape fusion module for producing an initial complete shape from multi-modality input (i.e., images and point clouds), and a follow-up shape consolidation module to obtain the final complete shape by discarding unreliable points introduced by the inconsistency from diffusion priors. Extensive experimental results demonstrate our superior performance, especially in recovering fine details.

Designing a functional occlusal surface for denture crowns is a complex and important task in prosthodontics. Manual design is time-consuming and heavily relies on the dentist's experience, as it requires careful consideration of occlusal function. Due to the limitations of manual design, the field has turned to data-driven methods for occlusal surface design. However, many of these methods neglect critical geometric details, such as normals and curvature, impacting the quality of the occlusal surface. In this paper, we introduce Diff-OSGN, a novel denture crown occlusal surface generation network based on a denoising diffusion model, which focuses on generating the detailed geometric structure of denture crowns. We model the occlusal surface as a geometry map based on the occlusal plane, incorporating height and normal maps rasterized from intra-oral crown scanning. Both maps represent occlusal surface geometry, and their combination further enhances these details. Considering the crucial occlusal information, we extract features from the geometry maps of adjacent and occlusal teeth, using them as conditions in the reverse diffusion process to train our network for optimal occlusal function. Additionally, we define three geometric operators and corresponding loss functions as constraints to better extract geometric features of the target occlusal surface, such as ridges and grooves, for adequate supervision. Our results demonstrate that Diff-OSGN provides quantitatively and qualitatively superior performance than competing baselines and state-of-the-art methods.

The article presents a new method for constructing self-supporting surfaces using arch beams that are designed to convert their thrust into supporting force, thereby eliminating shear stress and bending moments. Our method allows for the placement of the arch beams on the boundary or within a surface and partitions the surface into multiple self-supporting parts. The use of arch beams enhances stability and durability, adds aesthetic appeal, and allows for greater flexibility in the design process. We develop an iterative algorithm for designing self-supporting surfaces with arch beams that enables the user to control the shape of the beams and surface through intuitive parameters and specify the desired location of the arch beams. We verify the physical stability of the structure using finite element analysis. Experimental results show that our method can produce visually pleasing self-supporting surfaces that satisfy the equilibrium equation with high accuracy.

Tooth motion generation is an essential task in digital orthodontic treatment for precise and quick dental healthcare, which aims to generate the whole intermediate tooth motion process given the initial pathological and target ideal tooth alignments. Most prior works for multi-agent motion planning problems usually result in complex solutions. Moreover, the occlusal relationship between upper and lower teeth is often overlooked. In this paper, we propose a collaborative tooth motion diffusion model. The critical insight is to remodel the problem as a diffusion process. In this sense, we model the whole tooth motion distribution with a diffusion model and transform the planning problem into a sampling process from this distribution. We design a tooth latent representation to provide accurate conditional guides consisting of two key components: the tooth frame represents the position and posture, and the tooth latent shape code represents the geometric morphology. Subsequently, we present a collaborative diffusion model to learn the multi-tooth motion distribution based on inter-tooth and occlusal constraints, which are implemented by graph structure and new loss functions, respectively. Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in the application of orthodontics compared with state-of-the-art methods.

Automatic tooth alignment target prediction is vital in shortening the planning time of orthodontic treatments and aligner designs. Generally, the quality of alignment targets greatly depends on the experience and ability of dentists and has enormous subjective factors. Therefore, many knowledge-driven alignment prediction methods have been proposed to help inexperienced dentists. Unfortunately, existing methods tend to directly regress tooth motion, which lacks clinical interpretability. Tooth anatomical landmarks play a critical role in orthodontics because they are effective in aiding the assessment of whether teeth are in close arrangement and normal occlusion. Thus, we consider anatomical landmark constraints to improve tooth alignment results. In this article, we present a novel tooth alignment neural network for alignment target predictions based on tooth landmark constraints and a hierarchical graph structure. We detect the landmarks of each tooth first and then construct a hierarchical graph of jaw-tooth-landmark to characterize the relationship between teeth and landmarks. Then, we define the landmark constraints to guide the network to learn the normal occlusion and predict the rigid transformation of each tooth during alignment. Our method achieves better results with the architecture built for tooth data and landmark constraints and has better explainability than previous methods with regard to clinical tooth alignments.