Surface reconstruction from raw point clouds has been a pivotal focus in computer graphics for numerous years, primarily due to its integral role in modeling and rendering applications. A key approach to tackling this challenge involves establishing local geometries to align with the local curve. However, conventional methodologies typically resort to constructing either a local plane or a polynomial curve. While a local plane may result in the loss of sharp features, and boundary errors on open surfaces, utilizing a polynomial curve can pose difficulties when using neural networks due to issues with local coordinate consistency. In response to these limitations, we introduce a groundbreaking concept known as anglehedral surfaces to represent local geometry. This innovative idea offers enhanced flexibility in portraying sharp features and surface boundaries on open surfaces without necessitating a local coordinate system, which is crucial for neural network integration. Specifically, we leverage normals to construct anglehedral surfaces, encompassing dihedral and trihedral surfaces controlled by 2 and 3 normals, respectively. As a result, our approach adeptly accommodates sharp features on turning surfaces and accurately captures boundaries on open surfaces. We demonstrate superior performance on three widely-used datasets: ShapeNetCore, ABC, and ScanNet. Code is available at http://gitee.com/HuiTian1/3fold.
Point clouds are now a popular data representations in 3D vision research. However, due to the limited performance of sensors, the raw data usually suffer from sparsity, sensing noise, and incompleteness. This poses great challenges to downstream point cloud processing tasks. In recent years, deep-learning-based geometric point cloud enhancement methods are gaining tremendous research attention; they use deep neural networks with the aim of achieving dense, clean, and complete point clouds from low-quality raw point clouds. This paper presents a comprehensive survey of deep-learning-based geometric point cloud enhancement methods. It covers three main kinds of geometric point cloud enhancement: (i) denoising to achieve clean data, (ii) completion to recover unseen data, and (iii) upsampling to obtain dense data. Our survey presents a new taxonomy for recent state-of-the-art methods and systematic experimental results on standard benchmarks. In addition, we share our observations, and suggest future research directions for geometric point cloud enhancement with deep learning.
Parametric garment pattern models are widely used to generate 2D and 3D garment representations in reconstruction and simulation tasks. However, existing models are often difficult to adapt for specific purposes due to limited variations in base pattern shapes and the rigid, hardcoded handling of body and garment measurements. To address these challenges, we propose GarmentoPIA, a system that semi-automatically generates parametric garment pattern models from garment drafting literature using an LLM-based intelligent agent module. With GarmentoPIA, users can select garment drafting references and generate various pattern models that incorporate tailoring parameters specified in the source material. To improve adaptability across references, the system employs a prompt self-refinement mechanism that iteratively updates its instructions during model generation. This generation process is preceded by a manual preprocessing phase that first normalizes raw drafting instructions into a structured format. We furthermore introduce a garment domain-specific language (DSL), composed of explicit function calls corresponding to drafting components, to produce executable models that, once generated, run independently of any LLM—enhancing usability and accessibility. We validate the effectiveness of our approach through quantitative and qualitative evaluations.
Few-shot medical image segmentation (FSMIS) aims to achieve accurate segmentation with extremely limited labeled data. However, medical images are characterized by blurred boundaries, incomplete information, and high noise, leading to high uncertainty in model predictions. Most existing FSMIS methods, including recent descriptor-based state-of-the-art methods, generate segmentation results without explicitly modeling uncertainty at both the descriptor and prediction levels, which limits their reliability in complex regions. To address these problems, we propose DU-Net, a dual-level uncertainty-aware network designed to achieve reliable segmentation. Evidence extraction networks are incorporated to estimate the uncertainty of foreground and background descriptors. To suppress the interference of high-uncertainty descriptors, the descriptor-level uncertainty-aware similarity maps fusion module dynamically modulates the contribution of each descriptor during fusion. To enhance the prediction accuracy, our evidential dual-branch prediction module is designed to enable joint optimization of segmentation and evidential uncertainty estimation in a multi-task learning fashion. We impose consistency constraints on the predictions of the above two branches in low-uncertainty regions for mutual supervision. Experiments on three public medical image datasets demonstrate that our method outperforms current state-of-the-art methods.
This study introduces an extended reality (XR)-based interactive editing framework that allows users to sample colors directly from the physical world, apply them to virtual content, and register the results within the real environment. Existing augmented reality (AR) systems often suffer from scanning delays and low color fidelity, while virtual reality (VR) systems rely on predefined palettes that limit contextual creativity. Recent mixed reality (MR) authoring environments improve spatial continuity but still provide limited support for user-driven acquisition and direct editing of real-world colors or materials. To address these issues, we designed a cyclical interaction loop—sense, edit, display, and register—that integrates real world color sampling, 2D canvas editing, 3D result display, and spatial registration. We have evaluated the framework through three complementary studies: a within-subject comparison of AR, VR, and XR workflows, an XR ablation study, and a technical evaluation of reference-based color reproduction and XR sampling behavior. Findings showed that XR was associated with lower perceived workload and higher creativity support ratings than the scan-based AR workflow. Although XR showed longer task completion time than VR in the descriptive task time results, it received higher creativity support ratings and stronger hedonic-quality responses. Interviews emphasized the novelty and intuitiveness of sampling real-world colors and seeing them immediately reflected in physical space. These results suggest that XR's strengths are associated with the integration of real-world color sampling and contextual alignment in a unified creative experience. The proposed framework informs future XR authoring tools and possible applications in stage design, exhibition prototyping, and creative education.
