BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering
By Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, Dahua Lin. European Conference on Computer Vision, 2021
323 citations recorded by the source corpus.
Neural radiance fields (NeRF) has achieved outstanding performance in modeling 3D objects and controlled scenes, usually under a single scale. In this work, we focus on multi-scale cases where large changes in imagery are observed at drastically different scales. This scenario vastly exists in real-world 3D environments, such as city scenes, with views ranging from satellite level that captures the overview of a city, to ground level imagery showing complex details of an architecture; and can also be commonly identified in landscape and delicate minecraft 3D models. The wide span of viewing positions within these scenes yields multi-scale renderings with very different levels of detail, which poses great challenges to neural radiance field and biases it towards compromised results. To address these issues, we introduce BungeeNeRF, a progressive neural radiance field that achieves level-of-detail rendering across drastically varied scales. Starting from fitting distant views with a shallow base block, as training progresses, new blocks are appended to accommodate the emerging details in the increasingly closer views. The strategy progressively activates high-frequency channels in NeRF's positional encoding inputs and successively unfolds more complex details as the training proceeds. We demonstrate the superiority of BungeeNeRF in modeling diverse multi-scale scenes with drastically varying views on multiple data sources (city models, synthetic, and drone captured data) and its support for high-quality rendering in different levels of detail.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/795fecd949592a89c88cc96d22478df04519d4f8
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In Computational Mechanics
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Citations in the corpus
- NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review (2022, unknown): “Other methods published in first class conferences such as S-NeRF [130] (April 2021), BungeeNeRF [129] (December 2021), also deal with urban 3D reconstruction and view synthesis, albeit from remote sensing images.”
- VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction (2024, unknown): “Some methods [41, 44, 52, 53, 61] scale it to large scenes.”
- Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians (2024, methodology): “Inspired by the progressive training strategy commonly used in prior NeRF methods [28], [51], [56], we implement a coarse-to-fine optimization strategy. begins by training on a subset of anchors representing lower LOD levels and progressively activates finer LOD levels throughout optimization,…”