Saliency Guided Deep Neural Network for Color Transfer With Light Optimization.

Color transfer aims to change the color information of the target image according to the reference one. Many studies propose color transfer methods by analysis of color distribution and semantic relevance, which do not take the perceptual characteristics for visual quality into consideration. In this study, we propose a novel color transfer method based on the saliency information with brightness optimization. First, a saliency detection module is designed to separate the foreground regions from the background regions for images. Then a dual-branch module is introduced to implement color transfer for images. Finally, a brightness optimization operation is designed during the fusion of foreground and background regions for color transfer. Experimental results show that the proposed method can implement the color transfer for images while keeping the color consistency well. Compared with other existing studies, the proposed method can obtain significant performance improvement. The source code and pre-trained models are available at https://github.com/PlanktonQAQ/SCTNet.

MSL-Net: Sharp Feature Detection Network for 3D Point Clouds.

As a significant geometric feature of 3D point clouds, sharp features play an important role in shape analysis, 3D reconstruction, registration, localization, etc. Current sharp feature detection methods are still sensitive to the quality of the input point cloud, and the detection performance is affected by random noisy points and non-uniform densities. In this paper, using the prior knowledge of geometric features, we propose a Multi-scale Laplace Network (MSL-Net), a new deep-learning-based method based on an intrinsic neighbor shape descriptor, to detect sharp features from 3D point clouds. Firstly, we establish a discrete intrinsic neighborhood of the point cloud based on the Laplacian graph, which reduces the error of local implicit surface estimation. Then, we design a new intrinsic shape descriptor based on the intrinsic neighborhood, combined with enhanced normal extraction and cosine-based field estimation function. Finally, we present the backbone of MSL-Net based on the intrinsic shape descriptor. Benefiting from the intrinsic neighborhood and shape descriptor, our MSL-Net has simple architecture and is capable of establishing accurate feature prediction that satisfies the manifold distribution while avoiding complex intrinsic metric calculations. Extensive experimental results demonstrate that with the multi-scale structure, MSL-Net has a strong analytical ability for local perturbations of point clouds. Compared with state-of-the-art methods, our MSL-Net is more robust and accurate. The code is publicly available at.

KSS-ICP: Point Cloud Registration based on Kendall Shape Space.

Point cloud registration is a popular topic that has been widely used in 3D model reconstruction, location, and retrieval. In this paper, we propose a new registration method, KSS-ICP, to address the rigid registration task in Kendall shape space (KSS) with Iterative Closest Point (ICP). The KSS is a quotient space that removes influences of translations, scales, and rotations for shape feature-based analysis. Such influences can be concluded as the similarity transformations that do not change the shape feature. The point cloud representation in KSS is invariant to similarity transformations. We utilize such property to design the KSS-ICP for point cloud registration. To tackle the difficulty to achieve the KSS representation in general, the proposed KSS-ICP formulates a practical solution that does not require complex feature analysis, data training, and optimization. With a simple implementation, KSS-ICP achieves more accurate registration from point clouds. It is robust to similarity transformation, non-uniform density, noise, and defective parts. Experiments show that KSS-ICP has better performance than the state-of-the-art. Code1 and executable files2 are made public.

Triptolide protects against podocyte injury in diabetic nephropathy by activating the Nrf2/HO-1 pathway and inhibiting the NLRP3 inflammasome pathway.

Objectives: Diabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus. This study investigated the mechanism of triptolide (TP) in podocyte injury in DN.Methods: DN mouse models were established by feeding with a high-fat diet and injecting with streptozocin and MPC5 podocyte injury models were induced by high-glucose (HG), followed by TP treatment. Fasting blood glucose and renal function indicators, such as 24 h urine albumin (UAlb), serum creatinine (SCr), blood urea nitrogen (BUN), and kidney/body weight ratio of mice were examined. H&E and TUNEL staining were performed for evaluating pathological changes and apoptosis in renal tissue. The podocyte markers, reactive oxygen species (ROS), oxidative stress (OS), serum inflammatory cytokines, nuclear factor-erythroid 2-related factor 2 (Nrf2) pathway-related proteins, and pyroptosis were detected by Western blotting and corresponding kits. MPC5 cell viability and pyroptosis were evaluated by MTT and Hoechst 33342/PI double-fluorescence staining. Nrf2 inhibitor ML385 was used to verify the regulation of TP on Nrf2.Results: TP improved renal function and histopathological injury of DN mice, alleviated podocytes injury, reduced OS and ROS by activating the Nrf2/heme oxygenase-1 (HO-1) pathway, and weakened pyroptosis by inhibiting the nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasome pathway. In vitro experiments further verified the inhibition of TP on OS and pyroptosis by mediating the Nrf2/HO-1 and NLRP3 inflammasome pathways. Inhibition of Nrf2 reversed the protective effect of TP on MPC5 cells.Conclusions: Overall, TP alleviated podocyte injury in DN by inhibiting OS and pyroptosis via Nrf2/ROS/NLRP3 axis.

Intrinsic and Isotropic Resampling for 3D Point Clouds.

With rapid development of 3D scanning technology, 3D point cloud based research and applications are becoming more popular. However, major difficulties are still exist which affect the performance of point cloud utilization. Such difficulties include lack of local adjacency information, non-uniform point density, and control of point numbers. In this paper, we propose a two-step intrinsic and isotropic (I&I) resampling framework to address the challenge of these three major difficulties. The efficient intrinsic control provides geodesic measurement for a point cloud to improve local region detection and avoids redundant geodesic calculation. Then the geometrically-optimized resampling uses a geometric update process to optimize a point cloud into an isotropic or adaptively-isotropic one. The point cloud density can be adjusted to global uniform (isotropic) or local uniform with geometric feature keeping (being adaptively isotropic). The point cloud number can be controlled based on application requirement or user-specification. Experiments show that our point cloud resampling framework achieves outstanding performance in different applications: point cloud simplification, mesh reconstruction and shape registration. We provide the implementation codes of our resampling method at https://github.com/vvvwo/II-resampling.

Adaptively Isotropic Remeshing based on Curvature Smoothed Field.

With the development of 3D digital geometry technology, 3D triangular meshes are becoming more useful and valuable in industrial manufacturing and digital entertainment. A high quality triangular mesh can be used to represent a real world object with geometric and physical characteristics. While anisotropic meshes have advantages of representing shapes with sharp features (such as trimmed surfaces) more efficiently and accurately, isotropic meshes allow more numerically stable computations. When there is no anisotropic mesh requirement, isotropic triangles are always a good choice. In this paper, we propose a remeshing method to convert an input mesh into an adaptively isotropic one based on a curvature smoothed field (CSF). With the help of the CSF, adaptively isotropic remeshing can retain the curvature sensitivity, which enables more geometric features to be kept, and avoid the occurrence of obtuse triangles in the remeshed model as much as possible. The remeshed triangles with locally isotropic property benefit various geometric processes such as neighbor-based feature extraction and analysis. The experimental results show that our method achieves better balance between geometric feature preservation and mesh quality improvement compared to peers. We provide the implementation codes of our resampling method at https://github.com/vvvwo/Adaptively-Isotropic-Remeshing.

Approximate Intrinsic Voxel Structure for Point Cloud Simplification.

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 a novel point cloud simplification method, Approximate Intrinsic Voxel Structure (AIVS), to meet the diverse demands in real-world application scenarios. The method includes point cloud pre-processing (denoising and down-sampling), AIVS-based realization for isotropic simplification and flexible simplification with intrinsic control of point distance. To demonstrate the effectiveness of the proposed AIVS-based method, we conducted extensive experiments by comparing it with several relevant point cloud simplification methods on three public datasets, including Stanford, SHREC, and RGB-D scene models. The experimental results indicate that AIVS has great advantages over peers in terms of moving least squares (MLS) surface approximation quality, curvature-sensitive sampling, sharp-feature keeping and processing speed. The source code of the proposed method is publicly available. (https://github.com/vvvwo/AIVS-project).