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.

翻译：神经隐含 3D 表示方式已成为从多视图图像和合成新观点中重建表面的强大范例。 不幸的是, DVR 或 IDR 等现有方法需要精确的每像素对象面罩作为监督。 同时,神经弧度场也使新观点合成发生了革命性的变化。 然而, NERF 的估计体积密度不允许准确的表面重建。 我们的关键见解是,隐含表面模型和弧度场可以以统一的方式形成,使表层和体积都能够使用同一模型。 这种统一的观点使得新的、更有效的取样程序以及在没有输入面罩的情况下重建准确表面的能力得以实现。 我们比较了 DTU 、 BlendiveMVS 和合成室内数据集的方法。 我们的实验表明,在与IDR 相同的工作上,我们比NERF 质量要高,而不需要面具。