Title: Projection-based Point Convolution for Efficient Point Cloud Segmentation
Authors: Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, and Junmo Kim
Abstract: Understanding point cloud has recently gained huge interests following the development of 3D scanning devices and the accumulation of large-scale 3D data. Most point cloud processing algorithms can be classified as either point-based or voxel-based methods, both of which have severe limitations in processing time or memory, or both. To overcome these limitations, we propose a point convolutional module that uses 2D convolutions and multi-layer perceptrons (MLPs) as its components. As PPConv does not use point-based or voxel-based convolutions, it has advantages in fast point cloud processing. We demonstrate the efficiency of PPConv in terms of the trade-off between inference time and segmentation performance using S3DIS and ShapeNetPart dataset, and show that PPConv is the most efficient method among the compared ones.
