Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more attention recent years. However, most existing deep methods learn or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose novel multi-view network to address these problems, called Global Cross-view Feature Aggregation for Multi-View...

Objectives: Diabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus. This study investigated mechanism triptolide (TP) in podocyte injury DN.Methods: DN mouse models were established by feeding with a high-fat diet and injecting streptozocin MPC5 induced high-glucose (HG), followed TP treatment. Fasting blood glucose renal function indicators, such as 24 h urine albumin (UAlb), serum creatinine (SCr), urea nitrogen (BUN), kidney/body weight ratio mice...

Point cloud registration is a popular topic that has been widely used in 3D model reconstruction, location, and retrieval. In this paper, we propose new method, KSS-ICP, to address the rigid task Kendall shape space (KSS) with Iterative Closest (ICP). The KSS quotient removes influences of translations, scales, rotations for feature-based analysis. Such can be concluded as similarity transformations do not change feature. point representation invariant transformations. We utilize such...

Mesh reconstruction from a 3D point cloud is an important topic in the fields of computer graphic, vision, and multimedia analysis. In this paper, we propose voxel structure-based mesh framework. It provides intrinsic metric to improve accuracy local region detection. Based on detected regions, initial reconstructed can be obtained. With optimization our framework, optimized into isotropic one with geometric features such as external internal edges. The experimental results indicate that...

A point cloud as an information-intensive 3D representation usually requires a large amount of transmission, storage and computing resources, which seriously hinder its usage in many emerging fields. In this paper, we propose novel simplification method, Approximate Intrinsic Voxel Structure (AIVS), to meet the diverse demands real-world application scenarios. The method includes pre-processing (denoising down-sampling), AIVS-based realization for isotropic flexible with intrinsic control...

Convolutional Neural Networks and Transformers have good feature extraction but struggle in no-reference image quality assessment (NR - IQA) with real-world distortions. Vision Transformers, self-attention, capture global multi-scale features well. Local information entropy quantifies region complexity, aligning human perception. We propose IE ViT, an NR IQA model. It uses ViT for extraction, a dual-branch assessment, local better human-perception simulation, correlation loss. Experiments...

With rapid development of 3D scanning technology, point cloud based research and applications are becoming more popular. However, major difficulties still exist which affect the performance utilization. Such include lack local adjacency information, non-uniform density, control numbers. In this paper, we propose a two-step intrinsic isotropic (I&I) resampling framework to address challenge these three difficulties. The efficient provides geodesic measurement for improve region detection...

Due to the high dimensionality of point cloud data and irregularity complexity its geometric structure, effective attribute compression remains a very challenging task. Many recent efforts have focused on transforming clouds into images leveraging existing sophisticated image/video codecs improve coding efficiency. However, how synthesize coherent correlation-preserving is still inadequately addressed by studies, which are hindering exertion merits well-developed infrastructure. In this...

We propose a novel framework for 3D facial similarity measurement and its application in organization. The construction of the is based on Kendall shape space theory. quotient that constructed by features. In space, features can be measured robust to transformations. our framework, face represented feature landmarks model (FFLM), which regarded as utilize geodesic represent FFLM measurement, measurement. expressions, head poses, partial data. experiments, we compute distance between...

With the development of 3D digital geometry technology, triangular meshes are becoming more useful and valuable in industrial manufacturing entertainment. A high quality mesh can be used to represent a real world object with geometric physical characteristics. While anisotropic have advantages representing shapes sharp features (such as trimmed surfaces) efficiently accurately, isotropic allow numerically stable computations. When there is no requirement, triangles always good choice. In...

High-quality image generation is an important topic in digital visualization. As a sub-topic of the research, color transfer to produce high-quality with ideal scheme learned from reference one. In this article, we investigate mainstream methods provide survey that introduces related theories and frameworks. Such can be divided into three categories: statistical transfer, semantic-based for special target. For these technical routes, discuss research background, details, representative...

As an important subtopic of image enhancement, color transfer aims to enhance the scheme a source according reference one while preserving semantic context. To implement transfer, palette-based mapping framework was proposed. \textcolor{black}{It is classical solution that does not depend on complex analysis generate new scheme. However, usually requires manual settings, blackucing its practicality.} The quality traditional palette generation depends degree separation. In this paper, we...

With the advancement of 3D scanning technologies and deep learning theories, point cloud-based networks have gained considerable attention in fields vision computer graphics. Leveraging rich geometric information present clouds, these facilitate more accurate feature tasks. However, existing often suffer from generalization defects caused by variations pose inconsistent representations training data. In this paper, we propose a novel data augmentation framework to overcome limitations. Our...

Managing the level-of-detail (LOD) in architectural models is crucial yet challenging, particularly for effective representation and visualization of buildings. Traditional approaches often fail to deliver controllable detail alongside semantic consistency, especially when dealing with noisy inconsistent inputs. We address these limitations \emph{Co-LOD}, a new approach specifically designed LOD management modeling. Co-LOD employs shape co-analysis standardize geometric structures across...

Managing the level-of-detail (LOD) in architectural models is crucial yet challenging, particularly for effective representation and visualization of buildings. Traditional approaches often fail to deliver controllable detail alongside semantic consistency, especially when dealing with noisy inconsistent inputs. We address these limitations Co-LOD , a new approach specifically designed LOD management modeling. employs shape co-analysis standardize geometric structures across multiple...

Learning meaningful local and global information remains a challenge in point cloud segmentation tasks. When utilizing information, prior studies indiscriminately aggregates neighbor from different classes to update query points, potentially compromising the distinctive feature of points. In parallel, inaccurate modeling long-distance contextual dependencies when can also impact model performance. To address these issues, we propose GSTran, novel transformer network tailored for task. The...

As a significant geometric feature of 3D point clouds, sharp features play an important role in shape analysis, reconstruction, registration, localization, etc. Current detection methods are still sensitive to the quality input cloud, and performance is affected by random noisy points non-uniform densities. In this paper, using prior knowledge features, we propose Multi-scale Laplace Network (MSL-Net), new deep-learning-based method based on intrinsic neighbor descriptor, detect from clouds....