Additive depth maps, a compact approach for shape completion of single view depth maps

Abstract

In this paper we introduce a 2D convolutional neural network (CNN) which exploits the additive depth map, a minimal representation of volume, for reconstructing occluded portions of objects captured using commodity depth sensors. The additive depth map represents the amount of depth needed to transform the input into the “back” depth map taken with a sensor exactly opposite of the input camera. A union of the input and back depth map is then the completed 3D shape. To accomplish this task we employ a residual encoder-decoder with skip connections as the overall architecture. We train, and benchmark, our network using existing synthetic datasets as well as real world data captured from a commodity depth sensor. Our experiments show that the additive depth map, despite its minimal 2D representation of volume, can produce comparable results to existing state-of-the-art 3D CNN approaches for shape completion from single view depth maps.