论文标题
OmninerF：神经表面重建的杂交全向距离和辐射场
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction
论文作者
论文摘要
图像中的3D重建在虚拟现实和自动驾驶中具有广泛的应用，在此精确要求非常高。通过利用多层感知，在神经辐射场（NERF）中进行的突破性研究显着提高了3D对象的表示质量。后来的一些研究通过建立截短的签名距离场（TSDF）改善了NERF，但仍遭受3D重建中表面模糊的问题。在这项工作中，通过提出一种新颖的3D形状表示方式Omninerf来解决这种表面歧义。它基于训练Omni方向距离场（ODF）和神经辐射场的混合隐式场，用全向信息代替NERF中的明显密度。此外，我们在深度图上介绍了其他监督，以进一步提高重建质量。该提出的方法已被证明可以有效处理表面重建边缘的NERF缺陷，从而提供了更高质量的3D场景重建结果。
3D reconstruction from images has wide applications in Virtual Reality and Automatic Driving, where the precision requirement is very high. Ground-breaking research in the neural radiance field (NeRF) by utilizing Multi-Layer Perceptions has dramatically improved the representation quality of 3D objects. Some later studies improved NeRF by building truncated signed distance fields (TSDFs) but still suffer from the problem of blurred surfaces in 3D reconstruction. In this work, this surface ambiguity is addressed by proposing a novel way of 3D shape representation, OmniNeRF. It is based on training a hybrid implicit field of Omni-directional Distance Field (ODF) and neural radiance field, replacing the apparent density in NeRF with omnidirectional information. Moreover, we introduce additional supervision on the depth map to further improve reconstruction quality. The proposed method has been proven to effectively deal with NeRF defects at the edges of the surface reconstruction, providing higher quality 3D scene reconstruction results.
