Point cloud normal estimation is a fundamental task in 3D geometry
processing. While recent learning-based methods achieve notable advancements in
normal prediction, they often overlook the critical aspect of equivariance.
This results in inefficient learning of symmetric patterns. To address this
issue, we propose E3-Net to achieve equivariance for normal estimation. We
introduce an efficient random frame method, which significantly reduces the
training resources required for this task to just 1/8 of previous work and
improves the accuracy. Further, we design a Gaussian-weighted loss function and
a receptive-aware inference strategy that effectively utilizes the local
properties of point clouds. Our method achieves superior results on both
synthetic and real-world datasets, and outperforms current state-of-the-art
techniques by a substantial margin. We improve RMSE by 4% on the PCPNet
dataset, 2.67% on the SceneNN dataset, and 2.44% on the FamousShape dataset.

通过提出 E3-Net，引入一种高效的随机框架方法、高斯加权损失函数和感知层感知推理策略来实现点云法线估计的等变性，从而显著提高了模型的准确性。与当前最先进的技术相比，我们在两个合成数据集和一个真实世界数据集上的结果都更优秀，RMSE 分别提高了 4%、2.67% 和 2.44%。