Implicit surfaces via neural radiance fields (NeRF) have shown surprising accuracy in surface reconstruction. Despite their success in reconstructing richly textured surfaces, existing methods struggle with planar regions with weak textures, which account for the majority of indoor scenes. In this paper, we address indoor dense surface reconstruction by revisiting key aspects of NeRF in order to use the recently proposed Vector Field (VF) as the implicit representation. VF is defined by the unit vector directed to the nearest surface point. It therefore flips direction at the surface and equals to the explicit surface normals. Except for this flip, VF remains constant along planar surfaces and provides a strong inductive bias in representing planar surfaces. Concretely, we develop a novel density-VF relationship and a training scheme that allows us to learn VF via volume rendering By doing this, VF-NeRF can model large planar surfaces and sharp corners accurately. We show that, when depth cues are available, our method further improves and achieves state-of-the-art results in reconstructing indoor scenes and rendering novel views. We extensively evaluate VF-NeRF on indoor datasets and run ablations of its components.

翻译：基于神经辐射场（NeRF）的隐式表面表示在表面重建方面已展现出惊人的精度。尽管现有方法在重建纹理丰富的表面方面取得了成功，但它们对于纹理较弱的平面区域（这类区域构成了室内场景的主体）的处理仍存在困难。本文通过重新审视NeRF的关键方面，以利用最近提出的矢量场（VF）作为隐式表示，来解决室内稠密表面重建问题。VF被定义为指向最近表面点的单位矢量。因此，它在表面处方向发生翻转，并等于显式的表面法线。除了这种翻转外，VF在平面表面上保持恒定，并为表示平面表面提供了强有力的归纳偏置。具体而言，我们提出了一种新颖的密度-VF关系及相应的训练方案，使我们能够通过体渲染来学习VF。通过这种方式，VF-NeRF能够精确地建模大平面和尖锐拐角。我们证明，当深度线索可用时，我们的方法能进一步提升性能，并在室内场景重建和新视角渲染方面取得了最先进的结果。我们在室内数据集上对VF-NeRF进行了广泛评估，并对其各组成部分进行了消融实验。