Abstract 3D scanners often obtain partial point clouds due to occlusion and limitation of viewing angles. Point cloud completion aims at inferring the full shape of an object from an incomplete point set. Existing deep learning models either do not consider local information or easily degrade the sharp details of the input, thereby losing some existing structures. In this paper, we propose a high fidelity point cloud completion network using pointwise convolution, called FinerPCN. FinerPCN generates complete and fine point clouds in a coarse-to-fine manner. FinerPCN consists of two subnetworks: an encoder-decoder for generating a coarse shape and pointwise convolution for refining its local structure. By repeatedly feeding partial input into the second subnetwork, FinerPCN effectively considers local information and alleviates structural blur of input while maintaining global shape. Experimental results show that FinerPCN generates finer detailed completion results than state-of-the-art methods while successfully keeping the shape of the input.