Directed Ray Distance Functions for 3D Scene Reconstruction

Abstract

We present an approach for full 3D scene reconstruction from a single new image that can be trained on realistic non-watertight scans. Our approach uses a predicted distance function, since these have shown promise in handling complex topologies and large spaces. We identify and analyze two key challenges for predicting these implicit functions from an image that have prevented their success on 3D scenes from a single image. First, we show that predicting a conventional scene distance from an image requires reasoning over a large receptive field. Second, we analytically show that the optimal output of a network that predicts these distance functions is often not a distance function. We propose an alternate approach, the Direct Ray Distance Function (DRDF), that avoids both challenges. We show that a deep network trained to predict DRDFs outperforms all other methods quantitatively and qualitatively on 3D reconstruction on Matterport3D, 3DFront, and ScanNet