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
from 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 recent learning-based techniques. Moreover, we demonstrate lower
runtime, im-proved generalization capabilities, and better scalability to
larger scenes compared to existing methods.

介绍了一种新型的场景表示 ——DeepSurfels，它能够结合几何和外观信息，并且可以很好地更新外观信息并且易于与机器学习方法相结合。同时，我们还提出了一种端对端的可训练在线外观融合管线，它可以将 RGB 图像的信息融合到所提出的场景表示中，并且比传统的纹理映射方法以及最近的基于学习的技术都表现出更好的运行时间、推广能力和可扩展性。