Neural implicit 3D representations have emerged as a powerful paradigm for
reconstructing surfaces from multi-view images and synthesizing novel views.
Unfortunately, existing methods such as DVR or IDR require accurate per-pixel
object masks as supervision. At the same time, neural radiance fields have
revolutionized novel view synthesis. However, NeRF's estimated volume density
does not admit accurate surface reconstruction. Our key insight is that
implicit surface models and radiance fields can be formulated in a unified way,
enabling both surface and volume rendering using the same model. This unified
perspective enables novel, more efficient sampling procedures and the ability
to reconstruct accurate surfaces without input masks. We compare our method on
the DTU, BlendedMVS, and a synthetic indoor dataset. Our experiments
demonstrate that we outperform NeRF in terms of reconstruction quality while
performing on par with IDR without requiring masks.

该研究通过统一的方式制定了表面和体渲染，从而使得无需任何输入蒙版即可实现准确表面重建，并通过实验证明其能够在各方面都优于 NeRF 方法并与 IDR 方法相媲美。