This article describes a dense 3D reconstruction method based on video sequences. It mainly aims at the repeated projection problem

which exists in the 3D objects reconstruction from multiple depth maps. With the concept of an error cloud

a method called "optimization of fixed 3D points" is proposed to solve this problem. First

it uses depth rectification to improve the consistence. Then

it adopts a projection classification algorithm to classify all the projection points into the different error clouds which are characterized by the different 3D points through the many-to-one mapping relationship. At last

it computes the point coordinates for every cloud in 3D space by Gaussian distribution. Following the above

the point data are polygonized to recover the mesh of 3D objects. The final experimental results show that this method efficiently reduces the bad effect from the many-to-one mapping problem and it makes the reconstruction closer to the real object

which achieve a better effect.