Aerial-NeRF: Adaptive Spatial Partitioning and Sampling for Large-Scale Aerial Rendering (2405.06214v1)

Abstract: Recent progress in large-scale scene rendering has yielded Neural Radiance Fields (NeRF)-based models with an impressive ability to synthesize scenes across small objects and indoor scenes. Nevertheless, extending this idea to large-scale aerial rendering poses two critical problems. Firstly, a single NeRF cannot render the entire scene with high-precision for complex large-scale aerial datasets since the sampling range along each view ray is insufficient to cover buildings adequately. Secondly, traditional NeRFs are infeasible to train on one GPU to enable interactive fly-throughs for modeling massive images. Instead, existing methods typically separate the whole scene into multiple regions and train a NeRF on each region, which are unaccustomed to different flight trajectories and difficult to achieve fast rendering. To that end, we propose Aerial-NeRF with three innovative modifications for jointly adapting NeRF in large-scale aerial rendering: (1) Designing an adaptive spatial partitioning and selection method based on drones' poses to adapt different flight trajectories; (2) Using similarity of poses instead of (expert) network for rendering speedup to determine which region a new viewpoint belongs to; (3) Developing an adaptive sampling approach for rendering performance improvement to cover the entire buildings at different heights. Extensive experiments have conducted to verify the effectiveness and efficiency of Aerial-NeRF, and new state-of-the-art results have been achieved on two public large-scale aerial datasets and presented SCUTic dataset. Note that our model allows us to perform rendering over 4 times as fast as compared to multiple competitors. Our dataset, code, and model are publicly available at https://drliuqi.github.io/.

• The paper introduces a novel adaptive spatial partitioning strategy that segments large aerial scenes using drone pose data.
• It achieves up to 4x faster rendering with a pose-based selection mechanism that reduces computational overhead.
• Ground-relative adaptive sampling concentrates on actual structures, lowering GPU memory usage and eliminating unnecessary sampling.

Enhancing Aerial Scene Rendering with Aerial-NeRF

Introduction to Aerial-NeRF's Innovations

Aerial-NeRF presents a strategic enhancement over traditional Neural Radiance Fields (NeRF) models specifically tailored for large-scale aerial rendering. The crux of Aerial-NeRF lies in its innovative approach to spatial partitioning and adaptive sampling, addressing challenges like insufficient detail from high-altitude perspectives and inefficient memory usage.

Key Features and Techniques

Adaptive Spatial Partitioning

Standard NeRF models struggle with large-scale scenes due to limitations in GPU memory and computational feasibility. Aerial-NeRF mitigates this by:

• Dynamically partitioning the space based on drones' camera poses instead of uniformly dividing the scene. This allows the model to adapt to diverse flight trajectories and topographies.
• Region-specific NeRF training where each partitioned region trains a separate NeRF, enabling efficient memory usage and rendering of extensive scenes.

Pose-Based Spatial Selection for Rendering

During inference, selecting the appropriate NeRF for a given viewpoint is crucial for fast rendering. Aerial-NeRF employs:

• A pose-based selection mechanism where the similarity of poses (not intensive network computations) quickly identifies which NeRF to use for rendering a new viewpoint. This results in significantly faster rendering—up to four times compared to existing methods.

Innovative Adaptive Sampling Technique

Traditional sampling methods resulted in wasted computation as many sampled points would not contribute to the final image. Aerial-NeRF introduces:

• Ground-relative adaptive sampling: Depending on the drone’s altitude, the model adjusts the sampling strategy to ensure that points are concentrated around actual structures, not empty space. This method not only improves rendering accuracy but also reduces computational overhead.

Benchmarks and Results

Aerial-NeRF's performance was rigorously tested on various datasets, including two public large-scale aerial datasets and a novel dataset named SCUTic. Here are several highlights:

• The model consistently delivered superior rendering speeds, performing rendering tasks up to four times faster than other competitive approaches.
• Improved resource efficiency was noted, where the model required fewer sampling points and less memory, saving up to 2 GB in GPU resources.

Utility and Future Application

Aerial-NeRF substantially advances the application potential of neural rendering in large-scale environments:

• Drone-based mapping and surveying: Enhanced speed and efficiency directly translate to more rapid and detailed aerial surveys, useful in urban planning and environmental monitoring.
• Virtual tourism: With Aerial-NeRF, expansive historical and natural sites can be rendered in detailed 3D for virtual tours, benefiting the tourism industry.
• Future enhancements: Considering further integration with real-time data processing and AI-driven automated drones could push boundaries in dynamic, large-scale mapping tasks.

Conclusion

By rethinking spatial partitioning through drone pose analysis and refining sampling methodologies to focus on structural landmarks, Aerial-NeRF sets a new precedent for rendering large-scale aerial views. This approach not only expands the practical scope of NeRF applications but also opens pathways for its integration in various real-world applications needing large-scale, high-detail rendering capabilities.