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Following considerable development in 3D scanning technologies, many studieshave 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 visiontasks, existing works have not effectively applied them onto 3D vision tasks.In particular, segmentation has not been well studied because of the difficultyof dense prediction for each point, which requires rich feature representation.In this paper, we propose a simple and efficient architecture named pointprojection and back-projection network (PBP-Net), which leverages 2D CNNs forthe 3D point cloud segmentation. 3 modules are introduced, each of whichprojects 3D point cloud onto 2D planes, extracts features using a 2D CNNbackbone, and back-projects features onto the original 3D point cloud. Todemonstrate effective 3D feature extraction using 2D CNN, we perform variousexperiments including comparison to recent methods. We analyze the proposedmodules through ablation studies and perform experiments on object partsegmentation (ShapeNet-Part dataset) and indoor scene semantic segmentation(S3DIS dataset). The experimental results show that proposed PBP-Net achievescomparable performance to existing state-of-the-art methods.

PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation