  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.
