Learning Explicit Contact for Implicit Reconstruction of Hand-held Objects from Monocular Images

Reconstructing hand-held objects from monocular RGB images is an appealing yet challenging task. In this task, contacts between hands and objects provide important cues for recovering the 3D geometry of the hand-held objects. Though recent works have employed implicit functions to achieve impressive progress, they ignore formulating contacts in their frameworks, which results in producing less realistic object meshes. In this work, we explore how to model contacts in an explicit way to benefit the implicit reconstruction of hand-held objects. Our method consists of two components: explicit contact prediction and implicit shape reconstruction. In the first part, we propose a new subtask of directly estimating 3D hand-object contacts from a single image. The part-level and vertex-level graph-based transformers are cascaded and jointly learned in a coarse-to-fine manner for more accurate contact probabilities. In the second part, we introduce a novel method to diffuse estimated contact states from the hand mesh surface to nearby 3D space and leverage diffused contact probabilities to construct the implicit neural representation for the manipulated object. Benefiting from estimating the interaction patterns between the hand and the object, our method can reconstruct more realistic object meshes, especially for object parts that are in contact with hands. Extensive experiments on challenging benchmarks show that the proposed method outperforms the current state of the arts by a great margin.Comment: 17 pages, 8 figure

A critical review of a computational fluid dynamics (CFD)-based explosion numerical analysis of offshore facilities

In oil and gas industries, the explosive hazards receive lots of attention to achieve a safety design of relevant facilities. As a part of the robust design for offshore structures, an explosion risk analysis is normally conducted to examine the potential hazards and the influence of them on structural members in a real explosion situation. Explosion accidents in the oil and gas industries are related to lots of parameters through complex interaction. Hence, lots of research and industrial projects have been carried out to understand physical mechanism of explosion accidents. Computational fluid dynamics-based explosion risk analysis method is frequently used to identify contributing factors and their interactions to understand such accidents. It is an effective method when modelled explosion phenomena including detailed geometrical features. This study presents a detailed review and analysis of Computational Fluid Dynamics-based explosion risk analysis that used in the offshore industries. The underlying issues of this method and current limitation are identified and analysed. This study also reviewed potential preventative measures to eliminate such limitation. Additionally, this study proposes the prospective research topic regarding computational fluid dynamics-based explosion risk analysis

PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images

We present PyMAF-X, a regression-based approach to recovering a full-body parametric model from a single image. This task is very challenging since minor parametric deviation may lead to noticeable misalignment between the estimated mesh and the input image. Moreover, when integrating part-specific estimations to the full-body model, existing solutions tend to either degrade the alignment or produce unnatural wrist poses. To address these issues, we propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop in our regression network for well-aligned human mesh recovery and extend it as PyMAF-X for the recovery of expressive full-body models. The core idea of PyMAF is to leverage a feature pyramid and rectify the predicted parameters explicitly based on the mesh-image alignment status. Specifically, given the currently predicted parameters, mesh-aligned evidence will be extracted from finer-resolution features accordingly and fed back for parameter rectification. To enhance the alignment perception, an auxiliary dense supervision is employed to provide mesh-image correspondence guidance while spatial alignment attention is introduced to enable the awareness of the global contexts for our network. When extending PyMAF for full-body mesh recovery, an adaptive integration strategy is proposed in PyMAF-X to produce natural wrist poses while maintaining the well-aligned performance of the part-specific estimations. The efficacy of our approach is validated on several benchmark datasets for body-only and full-body mesh recovery, where PyMAF and PyMAF-X effectively improve the mesh-image alignment and achieve new state-of-the-art results. The project page with code and video results can be found at https://www.liuyebin.com/pymaf-x.Comment: An eXpressive extension of PyMAF [arXiv:2103.16507], Supporting SMPL-X, Project page: https://www.liuyebin.com/pymaf-

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning

IntroductionLeaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production.MethodIn this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods).ResultsThe average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed.DiscussionThe growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed

Stability and drug dissolution evaluation of Qingkailing soft/hard capsules based on multi-component quantification and fingerprint pattern statistical analysis

Purpose: To carry out a post-marketing evaluation of the stability and drug dissolution of Qingkailing soft/hard capsules.Methods: High performance liquid chromatography with diode array detection (HPLC-DAD) method was developed for the determination of three key ingredients (chlorogenic acid, geniposide and baicalin) and fingerprints of QKL soft/hard capsules. Stability tests were carried out based on long-term testing. The drug release profile of Qingkailing soft and hard capsules were studied using semi-bionic incubation experiments.Results: The linearity, precision, stability, repeatability and recovery of HPLC and fingerprint all met the requirements of CFDA. Stability data from long-term studies showed that within 6 months the contents of the three key ingredients in both soft and hard capsules remained > 90 %. However, fingerprint pattern statistical analysis showed that the soft capsule is more stable than the hard capsule. Furthermore, the key ingredients of the hard capsule dissolved much faster (p < 0.05) than from the soft capsule. The level of dissolved drug of hard capsule is about 4 times the rate of soft capsule, after a 4-h incubation in gastric lavage fluid. In intestinal lavage fluid, more than 90 % of chlorogenic acid, geniposide and baicalin of hard capsule were dissolved in 2 h, while the soft capsule displayed a 12 h sustained release. Fingerprint pattern statistical analysis also showed that most of the components of soft capsule dissolved after 8 h.Conclusion: Compared with the hard capsule, Qingkailing soft capsule has certain advantages in stability and drug dissolution, which may affect the biopharmaceutics and the clinical effects of the drug.Keywords: Qingkailing capsule, Chlorogenic acid, Geniposide, Baicalin, Fingerprint, Sustained release, Principal component analysi

fast soft shadow by depth peeling

ACM Spec. Interest Group Comput.; Graph. Interact. Tech. (SIGGRAPH)Soft shadow generation is a challenging problem in realistic rendering. Previous methods using shadow map or shadow volume work well for point light sources but are difficult to be extended to area lights. This paper presents a new method for fast soft shadow generation under dynamic area light sources. Our algorithm encodes the depth distribution of the scene into a coarse depth grid in a preliminary pass from the light point of view. In the second pass, the scene is rendered from the camera viewpoint to capture the frontmost layer. During deferred shading, the area light is sampled and the irradiance of each shaded pixel is accumulated along the ray. Experimental results demonstrate high quality soft shadows with interactive performance for dynamic scenes and lighting. © ACM 2010

DataSheet1_Loss of Cep72 affects the morphology of spermatozoa in mice.PDF

The centrosome regulates mammalian meiosis by affecting recombination, synapsis, chromosome segregation, and spermiogenesis. Cep72 is one of the critical components of the centrosome. However, the physiological role of Cep72 in spermatogenesis and fertility remains unclear. In this study, we identify Cep72 as a testis-specific expression protein. Although Cep72 knockout mice were viable and fertile, their sperms were morphologically abnormal with incomplete flagellum structures. Transcriptome analysis reveals significant differences in six genes (Gm49527, Hbb-bt, Hba-a2, Rps27a-ps2, Gm29647, and Gm8430), which were not previously associated with spermatogenesis. Overall, these results indicate that Cep72 participates in regulating sperm morphology and yet is dispensable for fertility in mice.</p

Table1_Loss of Cep72 affects the morphology of spermatozoa in mice.DOCX

The centrosome regulates mammalian meiosis by affecting recombination, synapsis, chromosome segregation, and spermiogenesis. Cep72 is one of the critical components of the centrosome. However, the physiological role of Cep72 in spermatogenesis and fertility remains unclear. In this study, we identify Cep72 as a testis-specific expression protein. Although Cep72 knockout mice were viable and fertile, their sperms were morphologically abnormal with incomplete flagellum structures. Transcriptome analysis reveals significant differences in six genes (Gm49527, Hbb-bt, Hba-a2, Rps27a-ps2, Gm29647, and Gm8430), which were not previously associated with spermatogenesis. Overall, these results indicate that Cep72 participates in regulating sperm morphology and yet is dispensable for fertility in mice.</p