Point cloud reconstruction must deal with the noise, the holes and uneven sampling problems in acquisition. Traditional methods lack the way to learn prior knowledge from large datasets of high-quality geometric models. This paper proposes an end-to-end reconstruction method by combining multi-view projection and implicit function. First the depth map is obtained by projecting the point cloud to multiple views. The convolution-based encoder transforms the depth map into feature space, at the same time denoising and complementing the feature. Implicit function is implemented with Multi-layer Perceptron (MLP), and is used to represent geometry. In the training process, the high-quality mesh is sampled, the encoder and implicit function are supervised by the pairing pointclouds and implicit values of sampling. Experiments are carried on several datasets to show the effectiveness of the new method. By comparing with traditional method and existing data-driven methods, it shows the new method significantly improve the reconstruction details while suppressing scanning noise, and complement the holes in more natural way. The pre-trained model is directly applicable to multi-view depth capturing system, and can automatically balance the degree of denoise and preservation of detail, and reconstruct a visually acceptable geometric surface on real data.