Improved Poisson Surface Reconstruction with Various Passive Visual Cues from Multiple Camera Views

Abstract

Poisson surface reconstruction with octree is widely used as the last step to retrieve the surface data from the point cloud. When the point cloud is generated by the triangulation of point correspondence in multiple images, the noisy positions of the 3D points and the inaccurate estimation of the normal vectors will impact the quality of the reconstructed surface. In this work, the mesh optimization using multiple visual cues will be applied to improve the output of the Poisson surface reconstruction. Usually, the active cues like shading and focusing require elaborate experimental setup, whereas the passive cues like silhouette and photometric property can be more easily acquired from the raw images. The experimental results show that adaptive integration of the multiple passive visual cues will deliver the surface mesh data with high quality. Besides, the optimization algorithm is easily to parallelize, as each vertex moves independently, which makes it appealing for the real-time 3D reconstruction system.