Li Qi, Song Yu, Jin Xiaoqian, Wu Yan, Zhang Hang, Zhao Di
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
Jilin Provincial International Joint Research Center of Brain Informatics and Intelligence Science, Changchun 130022, China.
Sensors (Basel). 2024 Dec 22;24(24):8196. doi: 10.3390/s24248196.
With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean-geodesic network (EGNet), which uses point cloud-voxel-mesh data to characterize detail, contour, and geodesic features, respectively. The EGNet performs feature fusion through Euclidean and geodesic branches. In the Euclidean branch, the features extracted from point cloud data compensate for the detail features lost by voxel data. In the geodesic branch, geodesic features from mesh data are extracted using inter-domain fusion and aggregation modules. These geodesic features are then combined with contextual features from the Euclidean branch, and the simplified trajectory map of the grid is used for up-sampling to produce the final semantic segmentation results. The Scannet and Matterport datasets were used to demonstrate the effectiveness of the EGNet through visual comparisons with other models. The results demonstrate the effectiveness of integrating Euclidean and geodesic features for improved semantic segmentation. This approach can inspire further research combining these feature types for enhanced segmentation accuracy.
随着服务机器人技术的发展，室内语义分割中对更高边界精度的需求不断增加。传统的使用点云和体素数据提取欧几里得特征的方法往往忽略测地线信息，降低了相邻物体的边界精度，并消耗大量计算资源。本研究提出了一种新颖的网络，即欧几里得-测地线网络（EGNet），它分别使用点云-体素-网格数据来表征细节、轮廓和测地线特征。EGNet通过欧几里得和测地线分支进行特征融合。在欧几里得分支中，从点云数据中提取的特征补偿了体素数据丢失的细节特征。在测地线分支中，使用域间融合和聚合模块从网格数据中提取测地线特征。然后将这些测地线特征与欧几里得分支的上下文特征相结合，并使用简化的网格轨迹图进行上采样以产生最终的语义分割结果。使用Scannet和Matterport数据集通过与其他模型的视觉比较来证明EGNet的有效性。结果表明，整合欧几里得和测地线特征以提高语义分割的有效性。这种方法可以激发进一步研究将这些特征类型结合起来以提高分割精度。
