Purely MLP-based neural radiance fields (NeRF-based methods) often suffer from underfitting with blurred renderings on large-scale scenes due to limited model capacity. Recent approaches propose to geographically divide the scene and adopt multiple sub-NeRFs to model each region individually, leading to linear scale-up in training costs and the number of sub-NeRFs as the scene expands. An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene with increased grid resolutions. However, the feature grid tends to be less constrained and often reaches suboptimal solutions, producing noisy artifacts in renderings, especially in regions with complex geometry and texture. In this work, we present a new framework that realizes high-fidelity rendering on large urban scenes while being computationally efficient. We propose to use a compact multiresolution ground feature plane representation to coarsely capture the scene, and complement it with positional encoding inputs through another NeRF branch for rendering in a joint learning fashion. We show that such an integration can utilize the advantages of two alternative solutions: a light-weighted NeRF is sufficient, under the guidance of the feature grid representation, to render photorealistic novel views with fine details; and the jointly optimized ground feature planes, can meanwhile gain further refinements, forming a more accurate and compact feature space and output much more natural rendering results.

翻译：基于纯MLP的神经辐射场（NeRF类方法）常因模型容量有限而在大规模场景中产生模糊渲染的欠拟合问题。近期方法将场景进行地理分割并采用多个子NeRF分别建模各区域，导致训练成本和子NeRF数量随场景扩展呈线性增长。另一种替代方案采用特征网格表示，该方法计算高效且可通过提高网格分辨率自然适配大规模场景。然而特征网格约束较弱，常收敛至次优解，尤其在几何与纹理复杂区域渲染中产生噪声伪影。本文提出一种新框架，能在实现大规模城市场景高保真渲染的同时保持计算高效性。我们采用紧凑型多分辨率地面特征平面表示粗粒度捕获场景，并通过另一个NeRF分支联合学习的姿态编码输入进行渲染补充。研究表明，这种整合能发挥两类方案的优势：在特征网格表示引导下，轻量化NeRF即可生成具有精细细节的照片级新视角渲染；联合优化的地面特征平面可同时获得进一步精炼，形成更精确紧凑的特征空间，输出更加自然的渲染结果。