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PCPNet: Learning Local Shape Properties from Raw Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2018)
In this paper, we propose PCPNET, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, ...
Generative Adversarial Image Super-Resolution Through Deep Dense Skip Connections
(The Eurographics Association and John Wiley & Sons Ltd., 2018)
Recently, image super-resolution works based on Convolutional Neural Networks (CNNs) and Generative Adversarial Nets (GANs) have shown promising performance. However, these methods tend to generate blurry and over-smoothed ...
Weakly Supervised Part-wise 3D Shape Reconstruction from Single-View RGB Images
(The Eurographics Association and John Wiley & Sons Ltd., 2020)
In order for the deep learning models to truly understand the 2D images for 3D geometry recovery, we argue that singleview reconstruction should be learned in a part-aware and weakly supervised manner. Such models lead to ...
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2023)
We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-shape attention mechanism to enable ...