6 skills found
autonomousvision / Monosdf[NeurIPS'22] MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
CyprienBosserelle / BG FloodNumerical model for simulating shallow water hydrodynamics on the GPU using an Adaptive Mesh Refinment type grid. The model was designed with the goal of simulating inundation (River, Storm surge or tsunami). The model uses a Block Uniform Quadtree approach that runs on the GPU but the adaptive/multi-resolution/AMR is being implemented and not yet operational. The core SWE engine and adaptivity has been inspired and taken from St Venant solver from Basilisk and the CUDA GPU memory model has been inspired by the work from Vacondio _et al._2017)
dslisleedh / IGConvIGConv: Implicit Grid Convolution for Multi-Scale Image Super-Resolution
zzr-idam / UHD Low Light Image EnhancementConvolutional neural networks (CNNs) have achieved unparalleled success in the single Low-light Image Enhancement (LIE) task. Existing CNN-based LIE models over-focus on pixel-level reconstruction effects, hence ignoring the theoretical guidance for sustainable optimization, which hinders their application to Ultra-High Definition (UHD) images. To address the above problems, we propose a new interpretable network, which capable of performing LIE on UHD images in real time on a single GPU. The proposed network consists of two CNNs: the first part is to use the first-order unfolding Taylor’s formula to build an interpretable network, and combine two UNets in the form of first-order Taylor’s polynomials. Then we use this constructed network to extract the feature maps of the low-resolution input image, and finally process the feature maps to form a multi-dimensional tensor termed a bilateral grid that acts on the original image to yield an enhanced result. The second part is the image enhancement using the bilateral grid. In addition, we propose a polynomial channel enhancement method to enhance UHD images. Experimental results show that the proposed method significantly outperforms state-of-the-art methods for UHD LIE on a single GPU with 24G RAM (100 fps).
lspatial / SptemExpThe approach of ensemble spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high resolutions. (1) Extraction of covariates from the satellite images such as GeoTiff and NC4 raster (e.g NDVI, AOD, and meteorological parameters); (2) Generation of temporal basis functions to simulate the seasonal trends in the study regions; (3) Generation of the regional monthly or yearly means of air pollutant concentration; (4) Generation of Thiessen polygons and spatial effect modeling; (5) Ensemble modeling for spatiotemporal mixed models, supporting multi-core parallel computing; (6) Integrated predictions with or without weights of the model's performance, supporting multi-core parallel computing; (7) Constrained optimization to interpolate the missing values; (8) Generation of the grid surfaces of air pollutant concentrations at high resolution; (9) Block kriging for regional mean estimation at multiple scales.
zzr-idam / UHD Multi Exposure Image Fusion AlgorithmUltra HD resolution multi-exposure image fusion algorithm, which employs an implicit function to generate a 3D LUT grid of arbitrary resolution to obtain a clear ultra HD image.
