Pyramid Point: A Multi-Level Focusing Network for Revisiting Feature Layers

We present a mcthod to lcarn a diverse group of object categories from an unordcrcd point set. We propose our Pyramid Point network, which uses a dense pyramid structure instead of thc traditional ’U’ shape, typically seen in semantic segmentation networks. This pyramid structure gives a second look, allowing thc network to revisit different layers creating from various leveis on the network, allowing for feature propagation. We introduce a Focused Kemel Point convolution (FKP Conv), which expands on the traditional point convolutions by adding an attention mcchanism to the kemel outputs. This FKP Conv increases our feature quality and allows us to weigh thc kemel outputs dynamically. These FKP Convs are the central part of our Recurrent FKP Bottlcneck block, which makes up thc backbonc of our encoder. With this distinct network, we demonstrate competitive performance on threc benchmark data sets.