Multi-view 3D Reconstruction of Ancient Xizang Architecture
Aiming at the problem of difficulty in obtaining high-resolution depth maps for Xizang ancient architecture due to matching errors caused by illumination,complex texture and other factors,a deep learning network with adaptive cost volume aggregation and reliable attentional depth refinement module is proposed.This deep learning network adopts a strategy of from rough to fine,introduces self-attention mechanism to enhance image feature extraction ability,and uses adaptive cost volume aggregation to reduce pixel matching errors.By using depth refinement to improve depth map accuracy and reduce cumulative errors,high-resolution depth maps are obtained through iteration.The experimental results show that the reconstructed image on the self-built dataset is complete and the texture is clear,and the accuracy error,integrity error and comprehensive error on the DTU dataset are 0.297 mm,0.347 mm and 0.322 mm,respectively.3D reconstruction based on multi-view can provide effective help for the study and protection of Xizang ancient architecture.
deep learning3D reconstructionmulti-view stereoadaptive methoddeep refinementancient Xizang architecture
万方数据
维普
