8457classical approaches [45]. The use of monocular depth and
segmentation as auxiliary data has also been explored with
unconstrained image collections [31] or using scene repre-
sentations with hash encodings [44]. In contrast, our work
Neuralangelo builds upon hash encodings [23] to recover
surfaces but without the need for auxiliary inputs used in
prior work [3, 5, 31, 44, 45]. Concurrent work [38] also
proposes coarse-to-fine optimization for improved surface
details, where a displacement network corrects the shape
predicted by a coarse network. In contrast, we use hierarchi-
cal hash grids and control the level of details based on our
analysis of higher-order derivatives.

3. Approach
Neuralangelo- High-Fidelity Neural Surface Reconstruction