In this paper, we propose Neuralangelo for high-fidelity
surface reconstruction (Fig. 1). Neuralangelo adopts In-
stant NGP as a neural SDF representation of the underlying
3D scene, optimized from multi-view image observations
via neural surface rendering [36]. We present two findings
central to fully unlocking the potentials of multi-resolution
hash encodings. First, using numerical gradients to compute
higher-order derivatives, such as surface normals for the
eikonal regularization [8, 12, 20, 42], is critical to stabilizing
the optimization. Second, a progressive optimization sched-
ule plays an important role in recovering the structures at
different levels of details. We combine these two key ingredi-
ents and, via extensive experiments on standard benchmarks
and real-world scenes, demonstrate significant improvements
over image-based neural surface reconstruction methods in
both reconstruction accuracy and view synthesis quality.
Neuralangelo- High-Fidelity Neural Surface Reconstruction