  Neural signed distance functions (SDFs) are emerging as an effective
representation for 3D shapes. State-of-the-art methods typically encode the SDF
with a large, fixed-size neural network to approximate complex shapes with
implicit surfaces. Rendering with 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. 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.
