School of Mechanical Engineering, Xi'an University of Science and Technology
CHN Energy Shendong Coal Group Co, . Ltd.

煤矿综采工作面巷道的数字化三维重建过程中需要完整且密集的煤壁点云数据。受遮挡、视角限制等因素影响，采集的综采工作面煤壁点云数据往往不完整且稀疏，影响下游任务，需进行煤壁点云修复和补全。目前缺少针对井下点云补全任务的数据集和网络模型，现有模型用于煤壁点云补全时存在点云密度分布不均匀、点云特征信息丢失等情况。针对上述问题，设计了一种基于残差优化的煤壁点云补全网络模型，采用监督学习方式学习点云特征信息，通过最小化密度采样和残差网络迭代优化输出完整点云。采集煤矿井下真实综采工作面煤壁点云数据，预处理后筛选可用数据，通过模拟随机空洞制作煤壁点云缺失数据集，并用缺失数据集训练基于残差优化的煤壁点云补全网络模型。实验结果表明：与经典的FoldingNet，TopNet，AtlasNet，PCN，3D−Capsule点云补全网络模型相比，基于残差优化的煤壁点云补全网络模型针对构造的缺失煤壁点云和稀疏煤壁点云补全的倒角距离、地移距离及F1分数均能达到最优水平，整体补全效果最佳；针对实际缺失的煤壁点云，该模型能够实现有效补全。

The digital 3D reconstruction process of coal mine fully mechanized working face roadways requires complete and dense coal wall point cloud data. Due to factors such as occlusion and limited viewing angle, the collected coal wall point cloud data of the fully mechanized working face is often incomplete and sparse, which affects downstream tasks and requires coal wall point cloud repair and completion. At present, there is a lack of datasets and network models for underground point cloud completion tasks. Existing models used for coal wall point cloud completion suffer from uneven distribution of point cloud density and loss of point cloud feature information. In order to solve the above problems, a coal wall point cloud completion network model based on residual optimization is designed. Supervised learning is used to learn point cloud feature information, and the complete point cloud is output by minimizing density sampling and iteratively optimizing the residual network. The method collects real coal wall point cloud data of fully mechanized working face underground, preprocesses and screens available data. The method creates a coal wall point cloud missing dataset by simulating random cavities. The missing dataset is used to train the residual optimization-based coal wall point cloud complementary network model. The experimental results show that compared with the classic FoldingNet, TopNet, AtlasNet, PCN, and 3D-Capsule point cloud completion network models, the residual optimization-based coal wall point cloud completion network model achieves the optimal level of chamfer distance, ground shift distance, and F1 score for the constructed missing and sparse coal wall point clouds, with the best overall completion effect. It is able to achieve effective completion for the actual missing coal wall point clouds.

国家重点研发计划子课题资助项目（2017YFC0804310）。

汪卫兵，侯学谦，赵栓峰，等. 基于残差优化的综采工作面煤壁点云补全方法[J]. 工矿自动化，2024，50（6）：120-128.

WANG Weibing, HOU Xueqian, ZHAO Shuanfeng, et al. A method for completing coal wall point cloud in fully mechanized working face based on residual optimization[J]. Journal of Mine Automation，2024，50（6）：120-128.