Figure 3.

Demonstration of DNFEB. The feature dimensions in DNFEB vary with their positions in the PSegNet, and in this figure, we only display feature dimensions of the 4^th DNFEB. A standard DNFEB contains three similar stages. The calculation process of stage 1 is enlarged in the lower part of the figure. On stage 1, for any point i in the feature space, we find its K-nearest neighbors in the initial XYZ space and in the current feature space, respectively. Secondly, position encoding is carried out for K-nearest neighbors in XYZ space to form a low-level feature encoding of the local region. At the same time, EdgeConv is carried out for the K-nearest neighbors in the current feature space to form a high-level feature representation of the local region. Finally, after concatenating the low-level and high-level local features, the new feature vector of the current point i is output after the calculation of the Attentive Pooling operation.