BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering (2112.05504v4)

Abstract: 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.

• The paper introduces a progressive training strategy that incrementally refines neural radiance fields from satellite to ground-level views.
• It employs a residual block architecture with modular expansion to capture fine details and boost PSNR, SSIM, and LPIPS metrics.
• The approach promises practical improvements for geospatial modeling, virtual tourism, and augmented reality through enhanced multi-scale rendering.

Overview of BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering

The paper presents BungeeNeRF, a novel approach to rendering neural radiance fields (NeRF) in scenarios that encompass a wide range of scales, from satellite-level views to ground-level imagery. Traditional NeRF models are typically constrained to single-scale environments, which poses limitations in representing 3D scenes captured at multiple scales. BungeeNeRF addresses these limitations through a progressive training strategy, effectively rendering both distant views and intricate details in closer views by progressively expanding the model's capabilities.

Core Contributions

BungeeNeRF introduces several innovative components to enhance NeRF's performance in multi-scale scenarios. Key contributions include:

• Progressive Data and Model Expansion: The model incrementally incorporates closer view data at each training stage, starting with the remote views. It progressively adds new layers to the model, which facilitates learning finer details as training advances.
• Residual Block Architecture: Instead of merely deepening the MLP, BungeeNeRF appends residual blocks at each stage. These blocks focus on the emerging details in the closer views, leveraging skip connections to access positional encodings.
• Inclusive Multi-level Supervision: The output heads of the model are supervised with images up to specific scales, which optimizes the network to render details progressively and guarantees consistent rendering quality across scales.

Technical Mechanisms

The neural architecture of BungeeNeRF is centered on modular growth and residual learning. The base block captures the broader, less detailed remote views, while subsequent blocks add complexity to the representation as closer views are introduced. The carefully designed residual block structure, with consistent re-exposure to positional encoding inputs, ensures that high-frequency details are captured as needed. This approach allows BungeeNeRF to achieve improved PSNR and SSIM metrics across various scales compared to standard NeRF and Mip-NeRF benchmarks.

Numerical Results and Empirical Validation

Experimentation on diverse datasets ranging from cityscapes to synthetic models demonstrates substantial improvements in rendering quality. BungeeNeRF exhibits notable gains in PSNR and perceptual fidelity metrics (LPIPS) while maintaining consistency in rendering details across different scales. These advancements underscore its applicability in real-world scenarios where viewing conditions vary dramatically, such as urban modeling and drone-captured landscapes.

Implications and Future Work

The development of BungeeNeRF has significant implications for fields reliant on high-quality virtual representations of real-world environments. The ability to accurately render scenes across large scale differences can vastly improve applications in geospatial modeling, virtual tourism, and augmented reality. Future work could explore integrating BungeeNeRF with dynamic scene modeling, potentially broadening its utility in depicting temporally evolving environments.

In conclusion, this paper provides a valuable contribution by enabling the representation and rendering of extreme multi-scale scenes, with a methodology that promises enhanced detail fidelity and completeness. The progressive training strategy and architectural innovations posited by BungeeNeRF establish a practical foundation for advanced neural volumetric modeling, suggesting new trajectories for future research in neural rendering technologies.