煤矿井下作业面临瓦斯爆炸、煤尘爆炸及火灾等安全隐患，这使得依赖三维重建技术的安全机器人研究成为重点方向。三维重建在复杂环境中的精准导航和检测至关重要，但传统探测方法在数据质量、精度及成本方面存在不足。为此，提出了一种名为2DGS-DbTrans的煤矿井下巷道三维重建方法，这是一种基于纯视觉的三维重建技术。为提升图像分辨率，设计了一个Transformer模块对输入图像进行增强处理，其中包含两个核心组件：多头深度卷积轴注意力机制和深度卷积门控网络。在处理流程中，首先利用Colmap生成稀疏点云，并通过二维高斯曲面来表示煤矿井下巷道环境，每个高斯曲面包含物体的坐标、颜色、大小和方向信息。此外，定义了两个损失函数，即颜色损失函数和道路平滑度损失函数。实验结果表明，2DGS-DbTrans方法在煤矿井下巷道环境的三维重建中，获得了优于其他方法的性能。

Underground coal mining faces hazards like gas explosions, coal dust explosions, and fires, underscoring the need for safety robotics based on 3D reconstruction. While essential for precise navigation and detection in complex environments, traditional methods fall short in data quality, accuracy, and cost. To address this, a 3D reconstruction method called 2DGS-DbTrans for underground coal mine tunnels was proposed, which is based on pure vision-based 3D reconstruction technology. To improve image resolution, a Transformer module was designed to enhance the input images, consisting of two core components: the multi-head depth convolutional axis attention mechanism and the deep convolutional gated network. In the processing workflow, sparse point clouds were first generated using Colmap, and the underground mine tunnel environment was represented by 2D Gaussian surfaces, where each Gaussian surface contains the coordinates, color, size, and orientation information of the objects. In addition, two loss functions were defined: the color loss function and the road smoothness loss function. Experimental results show that the 2DGS-DbTrans method outperforms other methods in 3D reconstruction of underground coal mine tunnels.