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
this http URL.

通过使用 2D 卷积和多层感知器作为组件的投影点卷积 (PPConv) 模块来优化点云处理算法，实现了在处理速度和分割性能之间的均衡。