KAPLAN: A 3D Point Descriptor for Shape Completion
This addresses shape completion for 3D vision applications, offering an efficient alternative to voxel-based methods, but it appears incremental as it builds on existing descriptor and convolution techniques.
The paper tackles 3D shape completion on unstructured point clouds by introducing KAPLAN, a descriptor that projects local neighborhoods onto multiple planes and uses 2D convolutions, achieving state-of-the-art performance on public datasets.
We present a novel 3D shape completion method that operates directly on unstructured point clouds, thus avoiding resource-intensive data structures like voxel grids. To this end, we introduce KAPLAN, a 3D point descriptor that aggregates local shape information via a series of 2D convolutions. The key idea is to project the points in a local neighborhood onto multiple planes with different orientations. In each of those planes, point properties like normals or point-to-plane distances are aggregated into a 2D grid and abstracted into a feature representation with an efficient 2D convolutional encoder. Since all planes are encoded jointly, the resulting representation nevertheless can capture their correlations and retains knowledge about the underlying 3D shape, without expensive 3D convolutions. Experiments on public datasets show that KAPLAN achieves state-of-the-art performance for 3D shape completion.