Multiresolution Tree Networks for 3D Point Cloud Processing

Understand

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks.

• Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions.
• This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training.
• The proposed tree-structured encoders can be used to classify shapes and outperform existing point-based architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset.