Neural Depth Field: Depth Estimators as Implicit Neural Fields for 3D Geometry Inpainting
Researchers introduce Neural Depth Field (NDF), a method that treats depth estimators as implicit neural fields to inpaint and reconstruct 3D scene geometry from incomplete data. NDF addresses issues of cross-view inconsistency and unreliable predictions by unifying depth estimation and implicit field fitting through a single test-time optimization. Experiments demonstrate that NDF achieves state-of-the-art performance, reducing cross-view inconsistency by 63.3% and improving inpainting accuracy by 23.1% across a range of scene types, including indoor scans and satellite imagery.
Why it matters: This approach offers a significant advance in producing globally consistent 3D geometry from incomplete data, with notable improvements in accuracy and consistency over previous methods.
Full story at: arXiv Computer Vision ↗