  The learning and aggregation of multi-scale features are essential in
empowering neural networks to capture the fine-grained geometric details in the
point cloud upsampling task. Most existing approaches extract multi-scale
features from a point cloud of a fixed resolution, hence obtain only a limited
level of details. Though an existing approach aggregates a feature hierarchy of
different resolutions from a cascade of upsampling sub-network, the training is
complex with expensive computation. To address these issues, we construct a new
point cloud upsampling pipeline called BIMS-PU that integrates the feature
pyramid architecture with a bi-directional up and downsampling path.
Specifically, we decompose the up/downsampling procedure into several
up/downsampling sub-steps by breaking the target sampling factor into smaller
factors. The multi-scale features are naturally produced in a parallel manner
and aggregated using a fast feature fusion method. Supervision signal is
simultaneously applied to all upsampled point clouds of different scales.
Moreover, we formulate a residual block to ease the training of our model.
Extensive quantitative and qualitative experiments on different datasets show
that our method achieves superior results to state-of-the-art approaches. Last
but not least, we demonstrate that point cloud upsampling can improve robot
perception by ameliorating the 3D data quality.
