Projection-Based Point Convolution for Efficient Point Cloud Segmentation

Pyunghwan Ahn,Chanho Lee,Eojindl Yi,Junmo Kim,Juyoung Yang

doi:10.1109/access.2022.3144449

Pyunghwan Ahn, Chanho Lee + Show 3 more

Open Access

https://doi.org/10.1109/access.2022.3144449

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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 Projection-based Point Convolution (PPConv), a point convolutional module that uses 2D convolutions and multi-layer perceptrons (MLPs) as its components. In PPConv, point features are processed through two branches: point branch and projection branch. Point branch consists of MLPs, while projection branch transforms point features into a 2D feature map and then apply 2D convolutions. As PPConv does not use point-based or voxel-based convolutions, it has advantages in fast point cloud processing. When combined with a learnable projection and effective feature fusion strategy, PPConv achieves superior efficiency compared to state-of-the-art methods, even with a simple architecture based on PointNet++. We demonstrate the efficiency of PPConv in terms of the trade-off between inference time and segmentation performance. The experimental results on S3DIS and ShapeNetPart show that PPConv is the most efficient method among the compared ones. The code is available at github.com/pahn04/PPConv.

Highlights

Recent developments in 3D scanning devices have incorporated a great amount of 3D data into machine vision applications, such as robotics, autonomous vehicles, and VR/AR
We propose Projection-based Point Convolution (PPConv), a point convolutional module for point cloud processing with 2D convolutions
We propose the 2D convolution-based point convolutional module, which can be used as a building block of a point-based network for point cloud processing

Summary

Introduction

Recent developments in 3D scanning devices have incorporated a great amount of 3D data into machine vision applications, such as robotics, autonomous vehicles, and VR/AR. Point cloud is a popular type of data with 3D geometry, and it is significantly beneficial to have a reliable autonomous point cloud processing system for better perception. Computer vision algorithms have achieved remarkable improvements in image recognition, 3dimensional data is naturally different from images that have only 2 spatial dimensions. Researchers have studied novel methods to deal with 3D data effectively. As convolutional neural networks (CNNs) have been proved to be effective in image recognition, researchers have attempted to extend the use of CNNs to 3D data processing. Several voxel-based methods [9], [27], [40] applied CNNs to 3D data, but this type of approach usually suffered from computational complexity of 3D convolutions. Point-based methods [18], [29], [30], [42], [46] could alleviate the exhaustive memory

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