ResNeRF：面向室内场景新视角合成的几何引导残差神经辐射场
ResNeRF: Geometry-Guided Residual Neural Radiance Field for Indoor Scene Novel View Synthesis
AI总结：
ResNeRF提出几何引导的两阶段框架，先基于SDF重建场景几何，再学习残差密度编码外观细节，从而在大规模室内场景中实现高保真新视角合成并达到最先进性能。
摘要：
我们提出了ResNeRF，一种新颖的几何引导两阶段框架，用于室内场景的新视角合成。考虑到良好的几何结构能够显著提升新视角合成的性能，同时为了避免几何歧义问题，我们提出基于由场景几何估计得到的基础密度和由几何参数化的残差密度来刻画场景的密度分布。在第一阶段，我们专注于基于SDF表示的几何重建，从而获得良好的场景几何表面以及清晰的密度。在第二阶段，基于第一阶段学习到的SDF来学习残差密度，以编码更多外观细节。通过这种方式，我们的方法能够借助几何先验更好地学习密度分布，在保持三维结构的同时实现高保真新视角合成。在包含大量少观测和无纹理区域的大规模室内场景上的实验表明，凭借良好的三维表面，我们的方法在新视角合成任务上达到了最先进的性能。
英文摘要：
We represent the ResNeRF, a novel geometry-guided two-stage framework for indoor scene novel view synthesis. Be aware of that a good geometry would greatly boost the performance of novel view synthesis, and to avoid the geometry ambiguity issue, we propose to characterize the density distribution of the scene based on a base density estimated from scene geometry and a residual density parameterized by the geometry. In the first stage, we focus on geometry reconstruction based on SDF representation, which would lead to a good geometry surface of the scene and also a sharp density. In the second stage, the residual density is learned based on the SDF learned in the first stage for encoding more details about the appearance. In this way, our method can better learn the density distribution with the geometry prior for high-fidelity novel view synthesis while preserving the 3D structures. Experiments on large-scale indoor scenes with many less-observed and textureless areas show that with the good 3D surface, our method achieves state-of-the-art performance for novel view synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
