Point Cloud Generation From Multiple Angles of Voxel Grids

The advancement of deep learning technology has been concentrating on deploying end-to-end solutions using high dimensional data, such as images. Recently, a number of methods have been proposed for reconstructing 3D objects using deep learning. One such technique is the method that involves recovering 3D objects as voxel grids using one or multiple images. However, there has been very little work directed towards the generation of 3D objects represented by a set of points, i.e. point cloud, from voxel grids which are ambiguous and coarse. The development of a deep learning model that generates point clouds with details from coarse voxel grids has numerous benefits to quality of life. For example, design professionals can use this model to generate detailed 3D point clouds using a sketched and coarse voxel to enable their creativities. This paper presents an algorithm to generate point clouds from voxel grids. The algorithm explicitly loads 3D objects into voxels without projection operations and associated information loss. To obtain a comprehensive understanding of the voxel grid, the grid is analyzed through various angles, as inspired by how humans observe 3D objects. The features from various angles are passed into a GRU layer to extract patterns across views, which will then be passed to a channel-wise convolutional layer and graph convolution to generated the predicted point cloud. The experimental result of the algorithm indicates that the algorithm is capable of generating high-quality point clouds by understanding the semantic features of the voxel grid.