Semantic Segmentation on 3D Point Clouds with High Density Variations
Ryan Faulkner Luke Haub Simon Ratcliffe Ian Reid Tat-Jun Chin
LiDAR scanning for surveying applications acquire measurements over wide
areas and long distances, which produces large-scale 3D point clouds with
significant local density variations. While existing 3D semantic segmentation
models conduct downsampling and upsampling to build robustness against varying
point densities, they are less effective under the large local density
variations characteristic of point clouds from surveying applications. To
alleviate this weakness, we propose a novel architecture called HDVNet that
contains a nested set of encoder-decoder pathways, each handling a specific
point density range. Limiting the interconnections between the feature maps
enables HDVNet to gauge the reliability of each feature based on the density of
a point, e.g., downweighting high density features not existing in low density
objects. By effectively handling input density variations, HDVNet outperforms
state-of-the-art models in segmentation accuracy on real point clouds with
inconsistent density, using just over half the weights.