  The efficient fusion of depth maps is a key part of most state-of-the-art 3D
reconstruction methods. Besides requiring high accuracy, these depth fusion
methods need to be scalable and real-time capable. To this end, we present a
novel real-time capable machine learning-based method for depth map fusion.
Similar to the seminal depth map fusion approach by Curless and Levoy, we only
update a local group of voxels to ensure real-time capability. Instead of a
simple linear fusion of depth information, we propose a neural network that
predicts non-linear updates to better account for typical fusion errors. Our
network is composed of a 2D depth routing network and a 3D depth fusion network
which efficiently handle sensor-specific noise and outliers. This is especially
useful for surface edges and thin objects for which the original approach
suffers from thickening artifacts. Our method outperforms the traditional
fusion approach and related learned approaches on both synthetic and real data.
We demonstrate the performance of our method in reconstructing fine geometric
details from noise and outlier contaminated data on various scenes.
