Orthodontic treatment needs regular tooth alignment checks, but current methods depend on clinic visits, limiting remote care. With the emergence of 3D Gaussian Splatting (3DGS), realistic novel views can be synthesized, making it possible for clinicians to remotely monitor orthodontic conditions. However, using only five intraoral images with unknown camera poses and dynamic lighting presents major challenges in dental applications. To address these challenges, we propose DentalGS, an enhanced 3DGS framework capable of synthesizing novel intraoral views from five post-orthodontic intraoral images and pre-orthodontic intraoral scan (IOS) data as prior, without camera poses. Our method initializes a Gaussian point cloud labeled with ISO-FDI tooth classes based on the patient’s pre-orthodontic IOS data, then estimates camera poses through iterative optimization. We introduce a Progressive Pair Generation Strategy as a data augmentation method that generates damage–repair image pairs to train a RepairNet, aiming to restore degraded geometry and appearance caused by the limited number of intraoral images. Additionally, we introduce a Lighting-Aware 3DGS inspired by physical reflectance properties to mitigate the effects of dynamic lighting conditions. Experimental results show that our method produces high-quality novel views while preserving geometric structure even under extreme viewpoints, offering an efficient and reliable solution for 3D tooth visualization in remote orthodontic monitoring.

Extracting wireframes from CAD models represented by point cloud remains a significant challenge in computer graphics. This difficulty arises from two main factors: first, imperfections in the point cloud data, such as lack of orientation, noise, and sparsity; and second, the inherent complexity of geometric shapes, which often feature a high density of sharp edges in close proximity. In this paper, we propose D-FRAME, a multi-stage wireframe extraction framework that incorporates a novel direction field to improve edge detection quality and connectivity, a refinement strategy to address sparse or noisy edge points, and a final coarse-to-fine connection module to extract a robust wireframe. The direction field not only facilitates connectivity but also enhances the precision of extracted edges by mitigating the impact of misclassified points. By combining the Restricted Voronoi Diagram (RVD) with the extracted wireframes and the original point cloud, our approach also achieves highly faithful reconstruction of CAD model. Experiments conducted on synthetic and real-world scanned CAD datasets demonstrate that D-FRAME effectively manages noise, sparsity, and complex geometries, yielding high-fidelity wireframes.

Although video deraining technology has achieved great success in recent years, extracting spatiotemporal feature representations across the domains of spatial and temporal in successive frames, then performing spatial and temporal modeling, and restoring high-quality deraining videos with rich details are still challenging tasks. In this paper, we use the hybrid Transformer for the first attempt in video rain removal tasks, and propose a novel video deraining network based on hybrid transformer (VDN-HT) to aggregate global and local representations to accomplish video deraining. In the feature extraction process, we propose to use a U-shaped structure based on serial Transformer blocks to extract shallow local features, deep global features and global dependencies, and then adaptively aggregate them to obtain rainy video features with rain streaks of different directions and densities. In order to better model spatiotemporal relationships, the VDN-HT uses the Transformer’s long-range and relational modeling abilities to obtain the features of spatial and the correlations of temporal between continuous video frames to achieve multi-frame alignment. For ensuring the global-local consistency of the reconstructed frames, we design a global-local reconstruction module composed of Transformer and convolutional neural network (CNN) in parallel to aggregate global and local information to better reconstruct each frame. In addition, the proposed gating-based refinement module and color loss effectively retain the details and color information after removing rain streaks. Extensive experiments on NTURain, RainSynLight25 and RainSynHeavy25 datasets have shown that the VDN-HT can handle many types of rainy videos and perform better than previous methods.

Tiny objects often have a small proportion of pixels in the image, leading to significant differences in the number of positive and negative samples and the lack of feature information. Accurately determining the position and category of tiny objects remains a huge challenge for object detection research. Therefore, we design an Adaptive Sample Assignment Strategy(ASAS) and tiny object focusing enhancement module to solve the above two problems. Specifically, starting from the study of positive and negative sample selection and balance strategies for tiny objects, we construct a lightweight Object Existence Probability Determination Network (OEPD/Net) to focus on the areas where tiny objects exist, and achieve adaptive assignment and balance of samples. A top/down, layer by layer focusing enhancement module is designed to effectively enhance the propagation ability of high/level semantic information for tiny objects. The above two solutions have excellent generalization and migration capabilities and can be applied to any stage and two-stage object detection network, effectively enhancing TOD performance. Finally, this article provides a performance analysis of detection performance the detection network based on the OEPD/Net output results, and demonstrates the effectiveness of the proposed OEPD-Net and focusing enhancement module through extensive experiments on a public dataset.

Abstract Automatic and accurate instance segmentation of teeth can provide important support for computer‐aided orthodontic work. Traditional methods for tooth segmentation studies often ignore the rich structural features of teeth. Capturing the complete and accurate geometry as well as morphological details of a single tooth remains a challenge for current tooth segmentation studies. In this article, a new tooth segmentation deeplearning network based on capturing dependencies and receptive field adjustment in cone beam computed tomography (CBCT) is proposed to achieve automatic and accurate instance segmentation of dental CBCT data. The method acquires coarse‐level features of tooth and accurate tooth centroids in the first stage, and acquires the instance information and spatial position localization of the tooth. The encoding process in the second stage of the network introduces a guidance module for obtaining tooth geometry information based on a 3D self‐attention mechanism to capture dependencies in CBCT. The proposed tooth feature integration module is based on multiscale fusion of dilated convolutions to capture tooth detailed information at multiple scales, and the network receptive field was adjusted. Extensive evaluation, ablation, and comparison experiments demonstrate that our method exhibits state‐of‐the‐art segmentation performance and accurate instance segmentation results, reflecting their potential applicability in clinical medicine.