3D multi views reconstruction of flame surface based on deep learning

Abstract A deep learning network based on MVSNet multi-view 3D reconstruction network (IM-MVSNet) is proposed in this paper for the reconstruction of the surface of laminar flame, which could suppress the influence of background noise on the reconstruction data when reconstructing the surface of laminar flame and improve the reconstruction accuracy of the flame surface at the same time. The network obtains high-quality segmented images by image segmentation of the reference frames and neighboring frames of the input sampled images to remove the background noise during the time of sampling, then reconstructs the multi-view images in 3D to build a 3D point cloud of the laminar flame surface, finally obtains the reconstructed laminar flame surface. The comparison of computational results using different reconstruction models shows that the 3D reconstruction network proposed in this paper could effectively reduce the point cloud noise of the reconstructed flame surface, improve the reconstruction accuracy of the flame surface, and provide a novel technical means for combustion research.