  Point cloud upsampling focuses on generating a dense, uniform and
proximity-to-surface point set. Most previous approaches accomplish these
objectives by carefully designing a single-stage network, which makes it still
challenging to generate a high-fidelity point distribution. Instead, upsampling
point cloud in a coarse-to-fine manner is a decent solution. However, existing
coarse-to-fine upsampling methods require extra training strategies, which are
complicated and time-consuming during the training. In this paper, we propose a
simple yet effective cascaded refinement network, consisting of three
generation stages that have the same network architecture but achieve different
objectives. Specifically, the first two upsampling stages generate the dense
but coarse points progressively, while the last refinement stage further adjust
the coarse points to a better position. To mitigate the learning conflicts
between multiple stages and decrease the difficulty of regressing new points,
we encourage each stage to predict the point offsets with respect to the input
shape. In this manner, the proposed cascaded refinement network can be easily
optimized without extra learning strategies. Moreover, we design a
transformer-based feature extraction module to learn the informative global and
local shape context. In inference phase, we can dynamically adjust the model
efficiency and effectiveness, depending on the available computational
resources. Extensive experiments on both synthetic and real-scanned datasets
demonstrate that the proposed approach outperforms the existing
state-of-the-art methods.
