# What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction Source: [What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:06:48.603Z Authors: Nilesh Kulkarni, Justin Johnson, David F. Fouhey Published: 2021-12-08T18:59:04.000Z ## Abstract We present an approach for full 3D scene reconstruction from a single unseen image. We train on dataset of realistic non-watertight scans of scenes. Our approach predicts a distance function, since these have shown promise in handling complex topologies and large spaces. We identify and analyze two key challenges for predicting such image conditioned distance functions that have prevented their success on real 3D scene data. 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 the network trained to predict these distance functions does not obey all the distance function properties. We propose an alternate distance function, the Directed Ray Distance Function (DRDF), that tackles both challenges. We show that a deep network trained to predict DRDFs outperforms all other methods quantitatively and qualitatively on 3D reconstruction from single image on Matterport3D, 3DFront, and ScanNet. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Scene Reconstruction on Hypersim (test)](): Scene Reconstruction; Hypersim (test); Accuracy: 54.4. ## Original sources and implementation [Original paper]() [PDF]()