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Created by Carlos Tojal, Daniel Carias, Luis Conde Bento, Hugo Costelha and Catarina Reis from Polytechnic Institute of Leiria.
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ND-Net++ is a PointNet++-based point cloud classification, part segmentation and semantic segmentation neural network. ND is an abbreviation for "Normal Distribution".
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PointNet++ adopts multiple sampling and grouping stages to hierarchically extract features while abstracting the point cloud. To reduce the size of the network, we propose a new architecture sharing the same hierarchical concept, but estimating normal distributions instead of performing sampling and grouping.
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The grouping stage of the PointNet++ generates $K$ groups with $N$ 3-dimensional neighbors ($B \times K \times N \times 3$ tensor). Instead, a normal distribution can represent a neighborhood of points by its mean 3-dimensional vector and its $3 \times 3$ covariance matrix only.
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