Accurate three-dimensional (3D) blast furnace burden surface topography is crucial for optimizing material distribution and furnace operation. However, harsh conditions within the furnace pose challenges to this 3D reconstruction, such as difficulties in matching feature points and sparse 3D point clouds. To overcome these challenges, we propose a 3D reconstruction method for the blast furnace burden surface using virtual camera array. Firstly, an ORB feature extraction and matching method based on bidirectional optical flow tracking is proposed to solve the impact of illumination changes and noise. Then, the virtual camera array is constructed to generate a sparse 3D point cloud from an image sequence. Finally, dense surface reconstruction of the burden surface is achieved based on the improved PMVS and the Poisson surface reconstruction method. Experimental results demonstrate that the proposed method can extract accurate feature points and reconstruct the accurate 3D topography of the burden surface.