processing a 3D point cloud to produce a sparse tensor;

feeding the sparse tensor as an input to each of a plurality of branches of an encoder of a neural network to produce a plurality of branch feature maps, N being a number of the plurality of branches, N being equal to or greater than 3, each ith branch respectively comprising i sequentially chained different encoder blocks to produce an ith branch feature map, i being an integer between 1 and N;

feeding the plurality of branch feature maps to a plurality of hierarchical attention blocks to generate a plurality of emphasized feature maps, wherein, for each pth branch of a 3rd to Nth branches, a pth branch feature map and a (p−2) th emphasized feature map are fed to a corresponding (p−1) th hierarchical attention block, the (p−2) th emphasized feature map is output by a (p−2) th hierarchical attention block, and wherein a first branch feature map and a second branch feature map are fed to a first hierarchical attention block;

feeding each emphasized feature map output by the plurality of hierarchical attention blocks to a spatial feature transformer to fuse each emphasized feature map of the plurality of hierarchical attention blocks and generate a fused feature map; and

processing the fused feature map and a final decoder block of a decoder to predict a class label for a plurality of points in the 3D point cloud.