Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
This addresses the need for robust 3D scene understanding in applications like robotics and autonomous driving, though it is an incremental improvement over existing methods by focusing on rotation invariance.
The paper tackles the problem of rotation sensitivity in 3D point cloud deep learning by introducing a novel convolution operator that achieves rotation invariance, resulting in high accuracy in object classification and segmentation tasks with consistent performance across arbitrary rotations.
Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks being trained with data augmented with rotations. In this paper, we introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. The well-known point ordering problem is also addressed by a binning approach seamlessly built into the convolution. This convolution operator then serves as the basic building block of a neural network that is robust to point clouds under 6DoF transformations such as translation and rotation. Our experiment shows that our method performs with high accuracy in common scene understanding tasks such as object classification and segmentation. Compared to previous works, most importantly, our method is able to generalize and achieve consistent results across different scenarios in which training and testing can contain arbitrary rotations.