Abstract

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. SDFs encode 3D surfaces with a function of position that returns the closest distance to a surface. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics applications. We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality. We represent implicit surfaces using an octree-based feature volume which adaptively fits shapes with multiple discrete levels of detail (LODs), and enables continuous LOD with SDF interpolation. We further develop an efficient algorithm to directly render our novel neural SDF representation in real-time by querying only the necessary LODs with sparse octree traversal. We show that our representation is 2-3 orders of magnitude more efficient in terms of rendering speed compared to previous works. Furthermore, it produces state-of-the-art reconstruction quality for complex shapes under both 3D geometric and 2D image-space metrics.

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Acknowledgments

We would like to thank Jean-Francois Lafleche, Peter Shirley, Kevin Xie, Jonathan Granskog, Alex Evans, and Alex Bie at NVIDIA for interesting discussions throughout the project. We also thank Peter Shirley, Alexander Majercik, Jacob Munkberg, David Luebke, Jonah Philion and Jun Gao for their help with paper editing.

Cite

Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes. Computer Vision and Pattern Recognition, 1 (1), Article 1, January 2021. 
@article{Takikawa:2021:NGLOD,
    title = {Neural Geometric Level of Detail: Real-time Rendering with Implicit {3D} Shapes},
    author = {Towaki Takikawa and
              Joey Litalien and
              Kangxue Yin and
              Karsten Kreis and
              Charles Loop and
              Derek Nowrouzezahrai and
              Alec Jacobson and
              Morgan McGuire and
              Sanja Fidler},
    journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2021},
    doi = {10.1109/cvpr46437.2021.01120}
}

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