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- W2912607492 abstract "Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reconstruction approaches directly predict the entire point cloud in a single stage. Although this technique can handle low-resolution point clouds, it is not a viable solution for generating dense, high-resolution outputs. In this work, we introduce DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution. Towards this end, we propose an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid. Our method generates point clouds that are accurate, uniform and dense. Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a light-weight and scalable architecture for predicting high-resolution outputs." @default.
- W2912607492 created "2019-02-21" @default.
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- W2912607492 date "2019-01-01" @default.
- W2912607492 modified "2023-10-10" @default.
- W2912607492 title "Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network" @default.
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- W2912607492 doi "https://doi.org/10.1109/wacv.2019.00117" @default.
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