Automatic teeth segmentation and labeling on dental models are basic tasks in computer-aided dentistry. Many existing works can achieve promising results in teeth segmentation, but they heavily rely on aligned input dental models, which leads to additional manual intervention. Moreover, tooth labeling is an essential task in digital dentistry for treatment planning (e.g., orthodontic), and is usually ignored in these methods. In this article, we propose an AlignNet for aligning dental models of arbitrary sizes and orientations automatically. Meanwhile, a multi-task hybrid learning network is designed that effectively plays the advantages of semantic segmentation and instance segmentation, and synergistically improves the performance of teeth point clouds segmentation and labeling. Particularly, for the teeth-gingival boundaries with large segmentation errors, we utilize the filtered curvature information as a constrained feature to detect the weak boundary more accurately. At last, we propose a DiffLoss and postprocessing step based on the dental arch to address the teeth classification problem. Through extensive evaluations of oral scanning models, our method is robust to handle dental model point clouds with arbitrary size and orientation, and outperforms state-of-the-art teeth segmentation and labeling methods, demonstrating its full automation and robustness in clinical practice.

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.

Point clouds acquired by 3D scanning devices are often sparse, noisy, and non-uniform, causing a loss of geometric features. To facilitate the usability of point clouds in downstream applications, given such input, we present a learning-based point upsampling method, i.e., iPUNet, which generates dense and uniform points at arbitrary ratios and better captures sharp features. To generate feature-aware points, we introduce cross fields that are aligned to sharp geometric features by self-supervision to guide point generation. Given cross field defined frames, we enable arbitrary ratio upsampling by learning at each input point a local parameterized surface. The learned surface consumes the neighboring points and 2D tangent plane coordinates as input, and maps onto a continuous surface in 3D where arbitrary ratios of output points can be sampled. To solve the non-uniformity of input points, on top of the cross field guided upsampling, we further introduce an iterative strategy that refines the point distribution by moving sparse points onto the desired continuous 3D surface in each iteration. Within only a few iterations, the sparse points are evenly distributed and their corresponding dense samples are more uniform and better capture geometric features. Through extensive evaluations on diverse scans of objects and scenes, we demonstrate that iPUNet is robust to handle noisy and non-uniformly distributed inputs, and outperforms state-of-the-art point cloud upsampling methods.