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We present a novel 3D shape completion method that operates directly onunstructured point clouds, thus avoiding resource-intensive data structureslike voxel grids. To this end, we introduce KAPLAN, a 3D point descriptor thataggregates local shape information via a series of 2D convolutions. The keyidea is to project the points in a local neighborhood onto multiple planes withdifferent orientations. In each of those planes, point properties like normalsor point-to-plane distances are aggregated into a 2D grid and abstracted into afeature representation with an efficient 2D convolutional encoder. Since allplanes are encoded jointly, the resulting representation nevertheless cancapture their correlations and retains knowledge about the underlying 3D shape,without expensive 3D convolutions. Experiments on public datasets show thatKAPLAN achieves state-of-the-art performance for 3D shape completion.

KAPLAN: A 3D Point Descriptor for Shape Completion