Urban Radiance Field Representation with Deformable Neural Mesh Primitives

• URL: http://arxiv.org/abs/2307.10776v1
• Date: Thu, 20 Jul 2023 11:24:55 GMT
• Title: Urban Radiance Field Representation with Deformable Neural Mesh Primitives
• Authors: Fan Lu, Yan Xu, Guang Chen, Hongsheng Li, Kwan-Yee Lin, Changjun Jiang
• Abstract summary: Deformable Neural Mesh Primitive(DNMP) is a flexible and compact neural variant of classic mesh representation. Our representation enables fast rendering (2.07ms/1k pixels) and low peak memory usage (110MB/1k pixels) We 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)
• Score: 41.104140341641006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/}.

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