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

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction