We show qualitative results in Fig. 3 and quantitative
results in Table 1. On average, Neuralangelo achieves the
lowest Chamfer distance and the highest PSNR, even without
using auxiliary inputs. The result suggests that Neuralangelo
is more generally applicable than prior work when recover-
ing surfaces and synthesizing images, despite not performing
best in every individual scene.

We further ablate Neuralangelo against the following
conditions: 1) AG: analytical gradients, 2) AG+P: analytical
gradients and progressive activating hash resolutions, 3) NG:
numerical gradients with varying ϵ. Fig. 4 shows the results
qualitatively. AG produces noisy surfaces, even with hash
resolutions progressively activated (AG+P). NG improves
Neuralangelo- High-Fidelity Neural Surface Reconstruction