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Figure 1: Our deep generative network DSG-Net encodes 3D shapes with complex structure and fine geometry in a representation that leverages the synergy between geometry and structure, while disentangling these two aspects as much as possible. This enables novel modes of controllable generation for ...
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To tackle this, we introduce DSG-Net, a deep neural network that learns a disentangled structured & geometric mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also...
天然裂缝网络建模(DecisionSpace® Natural Fracture Network) 为了描述储层中的不同裂缝系统的走向、倾角和分布,天然裂缝网络建模技术至关重要,其用来制定人工压裂的最佳方案,有效改造天然裂缝,建立油气流动通道。在建模过程中会用到成像测井解释数据,地震属性数据可以作为裂缝分布的约束数据。天然裂缝网格建模具有的特色功...
天然裂缝网络建模(DecisionSpace®NaturalFractureNetwork) 为了描述储层中的不同裂缝系统的走向、倾角和分布,天然裂缝网络建模技术至关重要,其用来制定人工压裂的最佳方案,有效改造天然裂缝,建立油气流动通道。在建模过程中会用到成像测井解释数据,地震属性数据可以作为裂缝分布的约束数据。天然裂缝网格建模具有的特色功能包括...
(Chen et al., 2021) proposed the Transformers Make Strong Encoders (TansUNet) network, which combines Transformer with CNN to catch global spatial feature information and local feature information at the same time. However, when CNN upsamples the feature map in the Decoder part, the limitation...