Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo reconstruction, most existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with few textures. To address these limitations, this paper presents a Detail-preserving and Content-aware Variational (DCV) multi-view stereo method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware $\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and regularization parameters based on the current input. It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing. Experimental results on benchmark datasets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse ring datasets in terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image processin

Fast Numerical Solutions of Gas-Particle Two-Phase Vacuum Plumes

The free molecule point source and Simons models coupled to the particle Lagrangian trajectory model are employed, respectively, to establish the fast solving method for gas-particle two-phase vacuum plumes. Density, velocity and temperature distributions of gas phase, and velocity and temperature of particles are solved to present the flow properties of two-phase plumes. The method based on free molecule point source model predicts the velocity and temperature distributions of vacuum plumes more reasonably and accurately than the Simons model. Comparisons of different drag coefficients show that Loth's drag formula can calculate exactly particle initial acceleration process for high Rer and Mr two-phase flows. The response characteristics of particles along their motion paths are further analyzed. Smaller particles can easily reach momentum equilibrium, while larger ones accelerate very difficultly. The thermal response is more relaxed than momentum response for different particle sizes. The present study is guidable to consider the effects of two-phase plumes on spacecraft in engineering

Effects of Flow Compressibility on Two-Phase Mixing in Supersonic Droplet-Laden Flows

This research addresses a numerical analysis on the effects of flow compressibility on the characteristics of droplet dispersion, evaporation, and mixing of fuel and air according to the simulation of the spatially developing supersonic shear flows laden with evaporating n-decane droplets. A sixth-order hybrid WENO numerical scheme is employed for capturing the unsteady wave structures. The influence of inflow convective Mach number (Mc), representing the high-speed flow compressibility, on the twophase mixing is analyzed, in which Mc is specified from 0.4 to 1.0. It is found that the shearing vortex is compressed spatially as Mc increases, associated with the alternate distributions of compression and expansion regimes in the flow field. The flow compressibility changes not only the vortex structures but also the aerothermal parameters of the shear flows, and further influences the dispersion and evaporation of droplets. The two-phase mixing efficiency is observed to decrease as Mc increases

Do VIP medical services damage efficiency? New evidence of medical institutions’ total factor productivity using Chinese panel data

This study examines the causal impact of very important person (VIP) medical services on hospital total factor productivity in Deyang, a prefectural-level city in western China, spanning the years 2015–2020. This aims to offer empirical evidence and policy recommendations for the implementation of VIP practices in the medical field. A secondary unbalanced panel dataset of 416 observations was collected from the annual reports of the Health Commission and 92 eligible medical institutions were included. This study utilized a two-stage strategy. First, the Global Malmquist index was used to calculate the total factor productivity and its decomposition terms for hospitals from 2015 to 2020. In the second stage, two-way fixed effects models and Tobit models were used to identify the relationship between VIP medical services and hospital efficiency; instrumental variables were used to solve potential endogeneity problems in the model. The results showed that VIP medical services had a significantly negative impact on medical institutions’ efficiency. The technological advances and pure technical efficiency related to VIP medical care may help explain these negative impacts, which were heterogeneous across groups divided by the nature of the hospital and the outside environment. It is imperative to prioritize the standardized provision of VIP medical services for medical institutions, optimize management and service process, enhance the training of clinical and scientific research capabilities of medical personnel, and scientifically allocate resources for both VIP and general medical services. This will help mitigate health inequality while improving the overall quality of medical services

Evaluation of the levofloxacin release characters from a rabbit foldable capsular vitreous body

The authors have manufactured a novel rabbit foldable capsular vitreous body (FCVB). The aim of this study was to determine whether this rabbit FCVB can release levofloxacin in vitro and in vivo, and to evaluate the release characteristics. In vitro, the rabbit FCVB with levofloxacin 500 μg/mL was immersed in cups of modified Franz diffusion cells. Following this, 200 μL of liquid was aspirated at intervals from 10 minutes to 24 hours. In vivo, the FCVB with levofloxacin was implanted into the right eyes of five rabbits. After implantation, the aqueous humor was aspirated on days 1, 7, 14, 28, and 56. The levofloxacin concentrations in the cups and aqueous humor samples were detected by high-performance liquid chromatography–tandem mass spectrometry. The FCVB was observed under a scanning electron microscope. The results showed that the released levofloxacin was stabilized at 20 ng/mL at time points from 10 minutes to 24 hours in vitro. In vivo, levofloxacin concentrations in the aqueous humor were 132, 50, 39, 11, and 15 ng/mL on days 1, 7, 14, 28, and 56, respectively. In the FCVB capsules, 300 nm apertures were observed. These results suggest the rabbit FCVB released levofloxacin stably in vitro and sustainably in vivo. This study provides a novel combined approach, with the FCVB as a vitreous substitute and drug delivery system for the treatment of bacterial endophthalmitis

Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion

Point clouds scanned by real-world sensors are always incomplete, irregular, and noisy, making the point cloud completion task become increasingly more important. Though many point cloud completion methods have been proposed, most of them require a large number of paired complete-incomplete point clouds for training, which is labor exhausted. In contrast, this paper proposes a novel Reconstruction-Aware Prior Distillation semi-supervised point cloud completion method named RaPD, which takes advantage of a two-stage training scheme to reduce the dependence on a large-scale paired dataset. In training stage 1, the so-called deep semantic prior is learned from both unpaired complete and unpaired incomplete point clouds using a reconstruction-aware pretraining process. While in training stage 2, we introduce a semi-supervised prior distillation process, where an encoder-decoder-based completion network is trained by distilling the prior into the network utilizing only a small number of paired training samples. A self-supervised completion module is further introduced, excavating the value of a large number of unpaired incomplete point clouds, leading to an increase in the network's performance. Extensive experiments on several widely used datasets demonstrate that RaPD, the first semi-supervised point cloud completion method, achieves superior performance to previous methods on both homologous and heterologous scenarios

D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field

Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with high levels of realism remains a challenge. The utilization of deep implicit function sparks a new era of image-based 3D clothed human reconstruction, enabling pixel-aligned shape recovery with fine details. Subsequently, the vast majority of works locate the surface by regressing the deterministic implicit value for each point. However, should all points be treated equally regardless of their proximity to the surface? In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface. This simple ``value to distribution'' transition yields significant improvements on nearly all the baselines. Furthermore, qualitative results demonstrate that the models trained using our uncertainty distribution loss, can capture more intricate wrinkles, and realistic limbs. Code and models are available for research purposes at https://github.com/psyai-net/D-IF_release

Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance.Comment: 19 pages, 7 figures, 4 table