R-ProjNet: an optimal rotated-projection neural network for wood segmentation from point clouds

ABSTRACT

This work aims to provide a deep learning framework to segment woods from tree point clouds. We develop a novel preprocessing layer before the classical sampling and convolution structure called the projection layer to organize 3D point clouds into 2D points. Input data are transformed into projection data along axis and planes for the subsequent convolution process, which helps decrease the complexity of networks. In order to obtain optimal and effective projection data for capturing local features, we formulate the 2D transformation in the learning process using two learnable angle parameters. The projection map is updated in the learning process for capturing geometric structure information, which plays an important role in wood point segmentation. Experiments show that we have achieved the loss and misclassification error of 0.41% and 8%, respectively, on wood points extraction from handheld laser scanning data. Besides, we also achieve the correctness, completeness and $F$-score of 90.4%, 91.5% and 0.91, respectively, in a public vehicle laser scanning dataset.

KEYWORDS:

• Deep learning
• laser scanning
• segmentation
• rotation
• projection

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No.62102184), in part by the Natural Science Foundation of Jiangsu Province (Grant No.BK20200784) and in part by China Postdoctoral Science Foundation (Grant No.2019M661852).