Source-linked AI summary
BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering
Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, Dahua Lin
TL;DR
Extreme scale variation leaves neural radiance fields uncertain and prone to compromised rendering across distant and close views. BungeeNeRF progressively grows both the training set and network, activating finer positional-encoding frequencies as views become closer. Across diverse multi-scale scenes, it produces superior coarse-to-fine rendering quality across scales, while remaining a building block rather than a comprehensive rendering system.
Problem
Neural radiance fields remain unclear in their ability to handle scenes with drastically varied scales, where views contain substantially different levels of detail and spatial coverage.
Method
BungeeNeRF progressively adds closer-scale training data and residual network blocks, using multi-level supervision and increasingly higher-frequency positional-encoding features.
Results
BungeeNeRF demonstrates superior results across various scenes, with high-quality rendering maintained across all scales compared with baselines.
Takeaways & Limitations
A single progressive model can provide hierarchical level-of-detail radiance representations for extreme multi-scale scenes.
Takeaways & Limitations
The method is presented as a building block that may need combination with orthogonal neural-rendering techniques for broader characteristics such as photorealism, dynamics, and editability.
Abstract
from arXiv · showhide
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.
1 Introduction
BungeeNeRF addresses the unresolved challenge of representing scenes across drastically varied scales by progressively learning a unified hierarchy of radiance representations. It achieves more complete remote views, finer close-view details, and level-of-detail rendering across scales.
- NeRF’s single-scale assumption remains insufficiently understood for scenes captured across drastically varied scales.
- City, synthetic, and delicate 3D scenes exhibit large visual changes as viewing distance alters geometric detail, texture resolution, and visible scene coverage.
- BungeeNeRF progressively expands training from remote to closer scales while synchronously growing the model with multiple level-of-detail output heads.
- Residual blocks predict color and density residuals between stages, while inclusive supervision exposes each block to images from remote scales through its corresponding scale.
- Experiments show more complete remote views and significantly more close-view details than vanilla NeRF and Mip-NeRF, which consistently fail under large scale changes.
2 Related Work
Related work extends neural radiance fields and multi-scale representations to broader settings, but existing approaches generally address different aspects or bounded scenes. BungeeNeRF instead targets extreme multi-scale scenes with a progressive radiance-field hierarchy.
- NeRF extensions address unbounded, dynamic, deformable, and phototourism scenes beyond simple static environments.
- Large-scene methods focus on horizontal urban span using multiple NeRF models with division and blending, whereas BungeeNeRF studies multi-scale representation through a new training paradigm.
- Mip-NeRF, windowed positional encoding, and BACON provide multi-scale properties through integrated encoding, coarse-to-fine training, or multiscale architectures.
- Other level-of-detail methods use bounded-scene grids, octrees, block-coordinate decompositions, or hash tables, while BungeeNeRF learns hierarchical radiance representations across scales.
3 BungeeNeRF
BungeeNeRF uses progressive data feeding and network growth to model extreme multi-scale scenes while matching scale-dependent detail frequencies. Its blocks provide increasingly detailed level-of-detail renderings within one model.
- 3.2 Challenges: Extreme multi-scale rendering creates inconsistent quality because close views cover only a subspace of remote views, while separate scale training causes discrepancies requiring fusion.
- 3.2 Challenges: Different scales require different positional-encoding frequencies: low frequencies represent remote views, while high frequencies recover close-view details.
- 3.3 Progressive Model with Multi-level Supervision: BungeeNeRF begins with remote views, adds one closer scale per stage, and appends residual blocks synchronized with the expanding training set.
- 3.3 Progressive Model with Multi-level Supervision: Each new block receives a positional-encoding skip connection and accesses higher-frequency channels to construct emerging details in closer views.
- 3.3 Progressive Model with Multi-level Supervision: Inclusive multi-level supervision trains each output head on samples from remote scales through its corresponding scale, unifying levels of detail in one controllable model.
4 Experiment
Experiments across city, synthetic, landscape, and UAV-captured scenes evaluate BungeeNeRF against NeRF variants and Mip-NeRF. Results show stronger multi-scale rendering, controllable level of detail, and benefits from progressive training and residual-block designs.
- Experimental Setup: BungeeNeRF is evaluated on Google Earth Studio city scenes, landscape scenes, Blender-synthetic scenes, and UAV-captured real-world scenes against NeRF, NeRF w/ WPE, and Mip-NeRF.The evaluation includes reconstructed and synthesized novel views, with ablations of progressive training and block connections.
- Quantitative Results: 0.5∼5 dB PSNR gains over Mip-NeRF are reported on two populated city scenes, especially at close scales rich in geometric detail.The quantitative results are from the final training phase on 56 Leonard and Transamerica scenes.
- Qualitative Results: BungeeNeRF produces clearer and more complete remote views while progressively adding scene details as the camera approaches the central target.Naively deepening the network leaves blurry artifacts at remote-view edges, whereas BungeeNeRF improves visual quality across scales and metrics.
- Mechanism Analysis: Higher-frequency positional-encoding channels become increasingly useful for constructing details, while manually truncating positional encoding performs worse under large-scale changes.The network learns to select from all frequency channels rather than using manually assigned frequency ranges for different blocks.
- Qualitative Results: Different output heads provide controllable LOD: earlier heads suit remote views, while later heads add finer geometric and texture details for close views.Earlier heads can reduce storage and rendering-time consumption when used for remote views.
- Ablation Study: Ablations show that removing inclusive multi-level supervision sharply degrades remote-scale performance, while omitting skip or residual connections slightly reduces performance across scales.Residual connections also help refine earlier heads and improve scene features used by later training phases.
5 Discussion and Conclusion
BungeeNeRF addresses drastic multi-scale variation by progressively growing both the model and training set, producing a hierarchy of scene representations from coarse to fine. Experiments across city, synthetic, and UAV scenes show high-quality rendering across scales, while broader integration with advanced rendering techniques remains future work.
- Discussion and Conclusion: BungeeNeRF synchronously grows its model and training set to learn hierarchical scene representations from coarse to fine.The approach targets scenes with large-scale variation in level of detail and field of view.
- Discussion and Conclusion: BungeeNeRF renders novel views across wide altitude changes in UAV scenes and recovers fine details in multi-dive city scenes.The reported examples cover UAV camera altitudes of 24∼76m and a three-stage city model.
- Discussion and Conclusion: BungeeNeRF produces results across four Blender Synthetic scales alongside Mip-NeRF and ground-truth references.The figure compares Mip-NeRF, four-stage BungeeNeRF, and ground-truth images from left to right.
- Discussion and Conclusion: BungeeNeRF is presented as a building block whose combination with orthogonal neural rendering techniques could improve rendering quality in future systems.The proposed future direction includes systems integrating characteristics such as large-scale, photorealistic, dynamic, and editable rendering.