  Following considerable development in 3D scanning technologies, many studies
have recently been proposed with various approaches for 3D vision tasks,
including some methods that utilize 2D convolutional neural networks (CNNs).
However, even though 2D CNNs have achieved high performance in many 2D vision
tasks, existing works have not effectively applied them onto 3D vision tasks.
In particular, segmentation has not been well studied because of the difficulty
of dense prediction for each point, which requires rich feature representation.
In this paper, we propose a simple and efficient architecture named point
projection and back-projection network (PBP-Net), which leverages 2D CNNs for
the 3D point cloud segmentation. 3 modules are introduced, each of which
projects 3D point cloud onto 2D planes, extracts features using a 2D CNN
backbone, and back-projects features onto the original 3D point cloud. To
demonstrate effective 3D feature extraction using 2D CNN, we perform various
experiments including comparison to recent methods. We analyze the proposed
modules through ablation studies and perform experiments on object part
segmentation (ShapeNet-Part dataset) and indoor scene semantic segmentation
(S3DIS dataset). The experimental results show that proposed PBP-Net achieves
comparable performance to existing state-of-the-art methods.
