Neural Radiance Fields (NeRFs) have achieved great success in the past few years. However, most current methods still require intensive resources due to ray marching-based rendering. To construct urban-level radiance fields efficiently, we design Deformable Neural Mesh Primitive~(DNMP), and propose to parameterize the entire scene with such primitives. The DNMP is a flexible and compact neural variant of classic mesh representation, which enjoys both the efficiency of rasterization-based rendering and the powerful neural representation capability for photo-realistic image synthesis. Specifically, a DNMP consists of a set of connected deformable mesh vertices with paired vertex features to parameterize the geometry and radiance information of a local area. To constrain the degree of freedom for optimization and lower the storage budgets, we enforce the shape of each primitive to be decoded from a relatively low-dimensional latent space. The rendering colors are decoded from the vertex features (interpolated with rasterization) by a view-dependent MLP. The DNMP provides a new paradigm for urban-level scene representation with appealing properties: $(1)$ High-quality rendering. Our method achieves leading performance for novel view synthesis in urban scenarios. $(2)$ Low computational costs. Our representation enables fast rendering (2.07ms/1k pixels) and low peak memory usage (110MB/1k pixels). We also present a lightweight version that can run 33$\times$ faster than vanilla NeRFs, and comparable to the highly-optimized Instant-NGP (0.61 vs 0.71ms/1k pixels). Project page: \href{https://dnmp.github.io/}{https://dnmp.github.io/}.

翻译：神经辐射场（NeRFs）在过去几年取得了巨大成功。然而，由于基于光线步进的渲染方式，目前大多数方法仍需消耗大量资源。为高效构建城市级辐射场，我们设计了可变形神经网格基元（DNMP），并提出用此类基元对整个场景进行参数化。DNMP是经典网格表示的一种灵活且紧凑的神经变体，兼具基于光栅化渲染的高效性与用于逼真图像合成的强大神经表示能力。具体而言，DNMP由一组连接的可变形网格顶点及其配对顶点特征构成，用于参数化局部区域的几何与辐射信息。为约束优化自由度并降低存储开销，我们强制每个基元的形状从相对低维的隐空间解码得到。渲染颜色由视角相关的MLP从（经光栅化插值的）顶点特征解码获得。DNMP为城市级场景表示提供了新范式，具有以下优越特性：（1）高质量渲染。我们的方法在城市场景的新视角合成中达到了领先性能。（2）低计算开销。我们的表示实现了快速渲染（2.07毫秒/千像素）和低峰值内存占用（110MB/千像素）。我们还推出了轻量级版本，其运行速度比标准NeRFs快33倍，且与高度优化的Instant-NGP相当（0.61 vs. 0.71毫秒/千像素）。项目页面：https://dnmp.github.io/