3d Point Cloud Completion Using Stacked Auto-Encoder For Structure Preservation

Abstract

3D point cloud completion problem deals with completing the shape from partial points. The problem finds its application in many vision-related applications. Here, structure plays an important role. Most of the existing approaches either do not consider structural information or consider structure at the decoder only. For maintaining the structure, it is also necessary to maintain the position of the available 3D points. However, most of the approaches lack the aspect of maintaining the available structural position. In this paper, we propose to employ stacked auto-encoder in conjunction a with shared Multi-Layer Perceptron (MLP). MLP converts each 3D point into a feature vector and the stacked auto-encoder helps in maintaining the available structural position of the input points. Further, it explores the redundancy present in the feature vector. It aids to incorporate coarse to fine scale information that further helps in better shape representation. The embedded feature is finally decoded by a structural preserving decoder. Both the encoding and the decoding operations of our method take care of preserving the structure of the available shape information. The experimental results demonstrate the structure preserving capability of our network as compared to the state-of-the-art methods.