Abstract
We present a detail-driven deep neural network for point
set upsampling. A high-resolution point set is essential for
point-based rendering and surface reconstruction. Inspired
by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end.
We propose a series of architectural design contributions
that lead to a substantial performance boost. The effect
of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show
that our method significantly outperforms the state-of-theart learning-based [58, 59], and optimazation-based [23]
approaches, both in terms of handling low-resolution inputs
and revealing high-fidelity details. The data and code are
at https://github.com/yifita/3pu.