Urban Radiance Field Representation with Deformable Neural Mesh Primitives Article Swipe
Fan Lü
,
Yan Xu
,
Guang Chen
,
Hongsheng Li
,
Kwan-Yee Lin
,
Changjun Jiang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.10776
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.10776
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.10776
- https://arxiv.org/pdf/2307.10776
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385004064
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385004064Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.10776Digital Object Identifier
- Title
-
Urban Radiance Field Representation with Deformable Neural Mesh PrimitivesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-20Full publication date if available
- Authors
-
Fan Lü, Yan Xu, Guang Chen, Hongsheng Li, Kwan-Yee Lin, Changjun JiangList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.10776Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.10776Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2307.10776Direct OA link when available
- Concepts
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Radiance, Rendering (computer graphics), Computer science, Pixel, Artificial intelligence, Vertex (graph theory), Computer vision, Computer graphics (images), Algorithm, Theoretical computer science, Remote sensing, Graph, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.budgets, | 118 |
| abstract_inverted_index.consists | 82 |
| abstract_inverted_index.features | 94, 143 |
| abstract_inverted_index.flexible | 52 |
| abstract_inverted_index.geometry | 98 |
| abstract_inverted_index.paradigm | 156 |
| abstract_inverted_index.pixels). | 196, 220 |
| abstract_inverted_index.powerful | 71 |
| abstract_inverted_index.provides | 153 |
| abstract_inverted_index.radiance | 29, 100 |
| abstract_inverted_index.vertices | 90 |
| abstract_inverted_index.(110MB/1k | 195 |
| abstract_inverted_index.0.71ms/1k | 219 |
| abstract_inverted_index.appealing | 162 |
| abstract_inverted_index.connected | 87 |
| abstract_inverted_index.constrain | 107 |
| abstract_inverted_index.construct | 27 |
| abstract_inverted_index.intensive | 19 |
| abstract_inverted_index.primitive | 125 |
| abstract_inverted_index.rendering | 68, 136, 187 |
| abstract_inverted_index.resources | 20 |
| abstract_inverted_index.synthesis | 175 |
| abstract_inverted_index.(2.07ms/1k | 188 |
| abstract_inverted_index.33$\times$ | 206 |
| abstract_inverted_index.Deformable | 34 |
| abstract_inverted_index.capability | 74 |
| abstract_inverted_index.comparable | 212 |
| abstract_inverted_index.deformable | 88 |
| abstract_inverted_index.efficiency | 65 |
| abstract_inverted_index.relatively | 131 |
| abstract_inverted_index.rendering. | 25, 166 |
| abstract_inverted_index.scenarios. | 178 |
| abstract_inverted_index.synthesis. | 78 |
| abstract_inverted_index.Instant-NGP | 216 |
| abstract_inverted_index.information | 101 |
| abstract_inverted_index.lightweight | 201 |
| abstract_inverted_index.performance | 171 |
| abstract_inverted_index.primitives. | 47 |
| abstract_inverted_index.properties: | 163 |
| abstract_inverted_index.urban-level | 28, 158 |
| abstract_inverted_index.High-quality | 165 |
| abstract_inverted_index.efficiently, | 31 |
| abstract_inverted_index.optimization | 113 |
| abstract_inverted_index.parameterize | 41, 96 |
| abstract_inverted_index.(interpolated | 144 |
| abstract_inverted_index.Specifically, | 79 |
| abstract_inverted_index.computational | 181 |
| abstract_inverted_index.marching-based | 24 |
| abstract_inverted_index.rasterization) | 146 |
| abstract_inverted_index.representation | 73, 160, 184 |
| abstract_inverted_index.view-dependent | 149 |
| abstract_inverted_index.low-dimensional | 132 |
| abstract_inverted_index.photo-realistic | 76 |
| abstract_inverted_index.representation, | 60 |
| abstract_inverted_index.highly-optimized | 215 |
| abstract_inverted_index.Primitive~(DNMP), | 37 |
| abstract_inverted_index.rasterization-based | 67 |
| abstract_inverted_index.\href{https://dnmp.github.io/}{https://dnmp.github.io/}. | 223 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |