Global-guided Focal Neural Radiance Field for Large-scale Scene Rendering (2403.12839v2)

Abstract: Neural radiance fields~(NeRF) have recently been applied to render large-scale scenes. However, their limited model capacity typically results in blurred rendering results. Existing large-scale NeRFs primarily address this limitation by partitioning the scene into blocks, which are subsequently handled by separate sub-NeRFs. These sub-NeRFs, trained from scratch and processed independently, lead to inconsistencies in geometry and appearance across the scene. Consequently, the rendering quality fails to exhibit significant improvement despite the expansion of model capacity. In this work, we present global-guided focal neural radiance field (GF-NeRF) that achieves high-fidelity rendering of large-scale scenes. Our proposed GF-NeRF utilizes a two-stage (Global and Focal) architecture and a global-guided training strategy. The global stage obtains a continuous representation of the entire scene while the focal stage decomposes the scene into multiple blocks and further processes them with distinct sub-encoders. Leveraging this two-stage architecture, sub-encoders only need fine-tuning based on the global encoder, thus reducing training complexity in the focal stage while maintaining scene-wide consistency. Spatial information and error information from the global stage also benefit the sub-encoders to focus on crucial areas and effectively capture more details of large-scale scenes. Notably, our approach does not rely on any prior knowledge about the target scene, attributing GF-NeRF adaptable to various large-scale scene types, including street-view and aerial-view scenes. We demonstrate that our method achieves high-fidelity, natural rendering results on various types of large-scale datasets. Our project page: https://shaomq2187.github.io/GF-NeRF/

• The paper proposes a two-stage training process that integrates a global coarse stage with a focal detail enhancement stage to capture comprehensive scene information.
• It leverages spatial and error guidance to direct local sub-encoders, ensuring consistent and high-fidelity rendering across large-scale scenes.
• GF-NeRF outperforms traditional block-partitioning methods by reducing training complexity and delivering coherent, photorealistic renderings for applications in VR/AR and simulation.

Global-Guided Focal Neural Radiance Field for Large-Scale Scene Rendering

Introduction to Neural Radiance Fields (NeRF) and Its Challenges in Large-Scale Scenes

Neural Radiance Fields have recently become a pivotal technique in photorealistic rendering of 3D scenes. Their inherent simplicity and impressive performance have found applications ranging from VR/AR to autonomous driving simulations. Despite these advances, applying NeRF to large-scale scenes often results in blurred renderings due to limited model capacity. Conventional approaches tackle this by partitioning the scene into blocks handled by separate sub-NeRF models. This divide-and-conquer strategy, while theoretically extending model capacity, introduces geometry and appearance inconsistencies across the scene.

Global-Guided Focal Neural Radiance Field (GF-NeRF)

This paper introduces a novel architecture, GF-NeRF, which pays attention to the high-fidelity rendering of large-scale scenes. GF-NeRF combines the strengths of global representation with focused local detail enhancement in a two-stage training process. The global stage aims to capture a coarse, continuous representation of the entire scene. The focal stage further decomposes the scene into blocks, employing sub-encoders that fine-tune based on global information, thereby significantly reducing training complexity and maintaining consistency throughout the scene.

Technical Innovations of GF-NeRF

GF-NeRF's design incorporates several key innovations:

• Two-Stage Training: Separation into global and focal stages promotes efficiency and detail.
• Global-Guided Training Strategy: Global information guides sub-encoders in the focal stage to focus on areas needing improvement.
• Spatial and Error Information Guidance: Helps sub-encoders capture detailed features of large-scale scenes without prior knowledge of the target scene's type.

Performance and Implications

GF-NeRF was demonstrated to outperform existing large-scale NeRF solutions, achieving more natural rendering results with superior fidelity. This approach brings several practical advantages. The reduced training complexity attributed to the pre-trained global stage accelerates the adoption of NeRF for large-scale applications. Moreover, GF-NeRF's framework signals a significant step toward rendering vast virtual worlds, leveraging the scalability and detail captured for applications in simulation, mapping, and beyond.

Future Directions

The findings from this work open several avenues for future exploration in AI and 3D rendering. Advancements could explore optimizing the training and rendering speed further, expanding the model's capacity to handle even larger scenes with minimal memory footprint. Investigating adaptive partitioning strategies that dynamically adjust based on scene content could also introduce efficiencies in model training and rendering quality.

Conclusion

In conclusion, Global-Guided Focal Neural Radiance Field (GF-NeRF) represents a significant advancement in rendering large-scale scenes with Neural Radiance Fields. By ingeniously combining global scene representation with localized detail enhancement, GF-NeRF not only improves rendering fidelity but also maintains coherence and reduces training complexity. This method's adaptability to various large-scale scene types without relying on prior knowledge underscores its potential to revolutionize how we create, interact with, and visualize digital worlds in 3D.