Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction
The 3D geometry of real-world scene data is often incomplete. Mainstream methods use depth estimators to inpaint missing structure. However, their prediction results can be inconsistent with observed geometry, or unreliable on out-of-distribution data. To solve these problems, we propose Neural Depth Field (NDF). Our key insight is that a depth estimator can also be a scene-level implicit field. As an estimator, it adapts to the target domain by learning observed depth data. As an implicit field, it fits the existing geometry to maintain consistency. Under this view, NDF addresses both problems through a single test-time optimization. Experiments show that NDF produces high-fidelity and g...
| Authors | Yingzhao Jian, Zihao Lin, Hehe Fan |
|---|---|
| Primary category | cs.CV |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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