  We present DeepSurfels, a novel hybrid scene representation for geometry and
appearance information. DeepSurfels combines explicit and neural building
blocks to jointly encode geometry and appearance information. In contrast to
established representations, DeepSurfels better represents high-frequency
textures, is well-suited for online updates of appearance information, and can
be easily combined with machine learning methods. We further present an
end-to-end trainable online appearance fusion pipeline that fuses information
provided by RGB images into the proposed scene representation and is trained
using self-supervision imposed by the reprojection error with respect to the
input images. Our method compares favorably to classical texture mapping
approaches as well as recently proposed learning-based techniques. Moreover, we
demonstrate lower runtime, improved generalization capabilities, and better
scalability to larger scenes compared to existing methods.
