CHORE: Contact, Human and Object REconstruction from a single RGB image

While most works in computer vision and learning have focused on perceiving 3D humans from single images in isolation, in this work we focus on capturing 3D humans interacting with objects. The problem is extremely challenging due to heavy occlusions between human and object, diverse interaction types and depth ambiguity. In this paper, we introduce CHORE, a novel method that learns to jointly reconstruct human and object from a single image. CHORE takes inspiration from recent advances in implicit surface learning and classical model-based fitting. We compute a neural reconstruction of human and object represented implicitly with two unsigned distance fields, and additionally predict a correspondence field to a parametric body as well as an object pose field. This allows us to robustly fit a parametric body model and a 3D object template, while reasoning about interactions. Furthermore, prior pixel-aligned implicit learning methods use synthetic data and make assumptions that are not met in real data. We propose a simple yet effective depth-aware scaling that allows more efficient shape learning on real data. Our experiments show that our joint reconstruction learned with the proposed strategy significantly outperforms the SOTA. Our code and models will be released to foster future research in this direction.Comment: 19 pages, 7 figure

Assisting classical paintings restoration : efficient paint loss detection and descriptor-based inpainting using shared pretraining

In the restoration process of classical paintings, one of the tasks is to map paint loss for documentation and analysing purposes. Because this is such a sizable and tedious job automatic techniques are highly on demand. The currently available tools allow only rough mapping of the paint loss areas while still requiring considerable manual work. We develop here a learning method for paint loss detection that makes use of multimodal image acquisitions and we apply it within the current restoration of the Ghent Altarpiece. Our neural network architecture is inspired by a multiscale convolutional neural network known as U-Net. In our proposed model, the downsampling of the pooling layers is omitted to enforce translation invariance and the convolutional layers are replaced with dilated convolutions. The dilated convolutions lead to denser computations and improved classification accuracy. Moreover, the proposed method is designed such to make use of multimodal data, which are nowadays routinely acquired during the restoration of master paintings, and which allow more accurate detection of features of interest, including paint losses. Our focus is on developing a robust approach with minimal user-interference. Adequate transfer learning is here crucial in order to extend the applicability of pre-trained models to the paintings that were not included in the training set, with only modest additional re-training. We introduce a pre-training strategy based on a multimodal, convolutional autoencoder and we fine-tune the model when applying it to other paintings. We evaluate the results by comparing the detected paint loss maps to manual expert annotations and also by running virtual inpainting based on the detected paint losses and comparing the virtually inpainted results with the actual physical restorations. The results indicate clearly the efficacy of the proposed method and its potential to assist in the art conservation and restoration processes

An optically pumped atomic clock based on a continuous slow cesium beam

Herein, we report the scheme of an optically pumped atomic clock based on a cold cesium atomic beam source. We propose the laser system and physical mechanism of this atomic clock, wherein the atomic beam travels in an upper parabolic trajectory, thereby eliminating the light shift effect. In the experiments, when the length of the free evolution region was 167 mm, the line width of the Ramsey fringe was 37 Hz. When the expected signal-to-noise ratio of the Ramsey fringe that can be achieved is 36,000, the expected short-term frequency stability is about 3.6 × 10–14/√τ, which is significantly higher than that of a conventional optically pumped cesium clock of similar volume

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Wire Arc Deposition Additive Manufacturing and Experimental Study of 316L Stainless Steel by CMT + P Process

The cold metal transfer plus pulse (CMT + P) process was performed to produce a 316L vertical wall through the single-channel multi-layer deposition method. The microstructure of different regions on deposited samples was observed by an optical microscope and a scanning electron microscope (SEM). The phase composition of the as-deposited wall was checked by X-ray diffraction, and the element distribution in the structure was analyzed by an energy-dispersive spectrometer. The tensile strength and microhardness of samples were tested, and the fracture morphology was observed by an SEM. Finally, the electrochemical corrosion characteristics of the as-deposited wall in different regions along the building direction were tested. Results from the experiments indicated that the microstructure of metallography showed a layer band. The metallurgical bounding between layers was carried out by dendrite remelting and epitaxial growth. Along the building direction, the alloy of different regions solidified in an ferritic-austenitic (FA) manner, and due to having undergone different heat histories, their SEM microstructures were significantly distinct. The ultimate tensile strength (UTS) and yield strength (YS) of the vertical specimens were higher than those of the horizontal specimens, displaying obvious anisotropy. Due to a large amount of precipitation of precipitated phases in terms of intermetallic compounds in the middle and upper regions, the tensile strength and microhardness along the building direction showed a trend of first decreasing and then increasing. In the bottom region, a small amount of ferrite precipitated in the austenite matrix, while in the middle of the as-deposited wall, the amount of ferrite gradually increased and was distributed in the austenite matrix as a network. However, due to the heat accumulation effect, the ferrite dissolved into austenite in large quantities and the austenite showed an obvious increase in size in the top region. A stable passivation film was caused by a relatively low dislocation density and grain boundary number, and the middle region of the arc as-deposited wall had the best corrosion resistance. The large consumption of chromium (Cr) atoms and material stripping in the top region resulted in the integrity of the passivation film in this region being the weakest, resulting in the lowest corrosion resistance

A method of brain computer cooperative navigation combined with simultaneous localization and mapping

Introducing human brain intelligence into robot system is an effective means to improve robot's cognition and decision-making ability. Aiming at the problems of human brain fatigue and the need of multi lead information in brain robot control, a brain computer cooperative navigation method combining synchronous localization and mapping (SLAM) is proposed in this paper. Through the steady-state visual evoked potential based on three leads, the image of the target area of interest of human brain is selected, and the brain computer cooperative navigation task is completed by combining SLAM and artificial potential field. The test results show that the average accuracy of the target area image selection method based on steady-state visual evoked potential is 94.17%, which proves that the three leads are effective. On this basis, the brain computer cooperative navigation method combined with SLAM is tested. The results show that the completion rate of navigation task is as high as 92.5%. This method alleviates the fatigue of human brain and reduces the hardware requirements of EEG acquisition

ESDA2008-59345 PRODUCT MODERN DESIGN PLATFORM TO SUPPORT PRODUCT DEVELOPMENT IN DISTRIBUTED RESOURCE ENVIRONMENT

ABSTRACT The manufacturers are competing against each other based on their development ability. A manufacturer who wants to do better than others must emphasize its ability of product development. The resources that product development relies on are more distributed than ever along with the varying of the global design environment. So more and more resources outside of enterprises are needed during the product design process. The platform supporting the product modern design under the circumstance of distributed resources will meet the requirement of the enterprise's product design and development under such conditions. The platform will aslo simplify the implementation on the integral of the IT support system for design resources outside of enterprises. In this paper the characteristics and supporting technologies of product design platform, which supporting the distributed design resource circumstance and centering on the enterprise, are studied. The building method of the platform is presented and a prototype of the product design platform is developed. Three subsystems are included in the platform. They are the product requirements analysis system, the product design planning system and the knowledge management system. Many design tasks can be supported on the platform, such as product requirement analysis, concept design, detail design, experiment, and maintenance knowledge acquirement. The distributing, implementing, tracking and managing of product lifecycle tasks can also be supported on the platform. The distributed design resources could be sealed as application components to provide design services. Design work flow model and knowledge flow model are built and controlled through the FIPER software. The design knowledge is managed based on the ontology theory. The virtual prototype of a complex product can be built and run more easily with the design platform. The process of building a virtual prototype could be described simply as following. Firstly the distributed models are sealed as application components. Then the components are published in the FIPER environment. Finally, the virtual prototype is built in the form of design work flow model in which the distributed components are integrated. To simulate the coupling of multidiscipline behaviors in a complex product design, the coupling formula is proposed to express the relations of different discipline behaviors. Based on the coupling formula, the coupling simulation can be run on the platform. Finally, a lifecycle performance prototype of an internal combustion engine is developed through the platform to verify the platform's functions