论文标题
深度曲面：学习在线外观融合
DeepSurfels: Learning Online Appearance Fusion
论文作者
论文摘要
我们介绍了深层曲折，这是一种新型的混合场景表示形式，用于几何和外观信息。深色弯曲结合了显式和神经构建块，共同编码几何和外观信息。与既定的表示形式相反，深板更好地表示高频纹理，非常适合在线外观信息更新，并且可以轻松地与机器学习方法结合使用。我们进一步提出了一个可端到端的可训练在线外观融合管道，该管道将RGB图像的信息融合到建议的场景表示中，并使用对输入图像的重新投影误差施加的自我训练。我们的方法与经典的纹理映射方法以及最新的基于学习的技术相比。此外，与现有方法相比，我们证明了较低的运行时，IM概括的概括功能以及更好地对更大场景的可扩展性。
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.
