Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

Rendering an accurate image of an isosurface in a volumetric field typically requires large numbers of data samples. Reducing the number of required samples lies at the core of research in volume rendering. With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion. In this paper, we investigate the use of such architectures for learning the upscaling of a low-resolution sampling of an isosurface to a higher resolution, with high fidelity reconstruction of spatial detail and shading. We introduce a fully convolutional neural network, to learn a latent representation generating a smooth, edge-aware normal field and ambient occlusions from a low-resolution normal and depth field. By adding a frame-to-frame motion loss into the learning stage, the upscaling can consider temporal variations and achieves improved frame-to-frame coherence. We demonstrate the quality of the network for isosurfaces which were never seen during training, and discuss remote and in-situ visualization as well as focus+context visualization as potential application

Intrinsic Cerebro-Cerebellar Functional Connectivity Reveals the Function of Cerebellum VI in Reading-Related Skills

Funding This work was supported by grants from the National Natural Science Foundation of China (NSFC: 31971036, 31971039, and 31571158).Peer reviewedPublisher PD

Perspective Transformer and MobileNets-Based 3D Lane Detection from Single 2D Image

Three-dimensional (3D) lane detection is widely used in image understanding, image analysis, 3D scene reconstruction, and autonomous driving. Recently, various methods for 3D lane detection from single two-dimensional (2D) images have been proposed to address inaccurate lane layouts in scenarios (e.g., uphill, downhill, and bumps). Many previous studies struggled in solving complex cases involving realistic datasets. In addition, these methods have low accuracy and high computational resource requirements. To solve these problems, we put forward a high-quality method to predict 3D lanes from a single 2D image captured by conventional cameras, which is also cost effective. The proposed method comprises the following three stages. First, a MobileNet model that requires low computational resources was employed to generate multiscale front-view features from a single RGB image. Then, a perspective transformer calculated bird’s eye view (BEV) features from the front-view features. Finally, two convolutional neural networks were used for predicting the 2D and 3D coordinates and respective lane types. The results of the high-reliability experiments verified that our method achieves fast convergence and provides high-quality 3D lanes from single 2D images. Moreover, the proposed method requires no exceptional computational resources, thereby reducing its implementation costs

Analysis of Synchronous Generator Self-Excitation under Capacitive Load Condition in Variable-Frequency Aviation Power System

As power electronic converters become more widely used in aviation power systems, the associated capacitive loads in the harmonic filter circuits increase accordingly. The risk of self-excitation of aeronautical synchronous generators due to capacitive loads is thus increased. Compared with the self-excitation of a generator in a conventional fixed-frequency power system, this process is more complicated in a variable-frequency aviation power supply (360–800 Hz), as both the varied frequency and the loading conditions contribute to the self-excitation. To quantify this effect, in our study, a series-parallel model of simplified RLC loads under a variable-frequency power supply was built. The criterion of generator self-excitation, given in terms of the generator impedance and the load impedance, was then derived. To facilitate the load configuration design in the case of an aviation power system, a comprehensive analysis of the influences of the varied load power and system frequency on the load impedance was conducted. A graphical approach was proposed to determine self-excitation by comparing the series load reactance and resistor with three critical impedances corresponding to three self-excitation criteria, which is more intuitive and enables one to visualize the tendency of self-excitation with varied frequencies and loading conditions more effectively. Finally, the influence of variable frequency on the self-excitation of the aeronautical synchronous generator was verified by the simulation results