基于特征聚合的深度霍夫变换法向估计算法

Deep Hough Transform Normal Estimation Algorithm Based on Feature Aggregation

摘要: 在点云处理过程中，法向估计是非常重要的一步。现有的深度霍夫变换的法向估计网络通过对点云进行霍夫变换得到邻域特征，再将其输入至卷积神经网络中学习估计法向。但由于霍夫变换过程中存在一定信息损失导致最后所得法向不准确，效果不够理想。对此，本文先通过霍夫变换将法向空间与二维平面相对应，并将二维空间离散化获得所有潜在切平面，设计特征聚合将点特征转化为潜在切平面特征作为CNN输入来降低霍夫变换过程中信息的损失，从而提升卷积神经网络的输入，进而提升网络整体的法向估计质量。实验结果表明，由此产生的法向估计网络的整体性能有所提升，对于不同噪声尺度也更具鲁棒性。

Abstract: In the process of point cloud processing, normal estimation is a very important step. The existing normal estimation network of deep Hough transform obtains neighborhood features by performing Hough transform on the point cloud and then inputs them into a convolutional neural network to learn and estimate the normal. However, due to certain information loss in the Hough transform process, the finally obtained normal is inaccurate and the effect is not ideal. In response to this, this paper first corresponds the normal space to a two-dimensional plane through Hough transform, and discretizes the two-dimensional space to obtain all potential tangent planes. Feature aggregation is designed to transform point features into potential tangent plane features as the input of CNN to reduce the information loss in the Hough transform process, thereby enhancing the input of the convolutional neural network and further improving the overall normal estimation quality of the network. Experimental results show that the overall performance of the resulting normal estimation network is improved, and it is also more robust to different noise scales.