Block-NeRF: Scalable Large Scene Neural View Synthesis (2202.05263v1)

Abstract: We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to decompose the scene into individually trained NeRFs. This decomposition decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment. We adopt several architectural changes to make NeRF robust to data captured over months under different environmental conditions. We add appearance embeddings, learned pose refinement, and controllable exposure to each individual NeRF, and introduce a procedure for aligning appearance between adjacent NeRFs so that they can be seamlessly combined. We build a grid of Block-NeRFs from 2.8 million images to create the largest neural scene representation to date, capable of rendering an entire neighborhood of San Francisco.

• The paper introduces Block-NeRFs, which divide large scenes into independently trainable neural radiance fields for scalable rendering.
• The model integrates appearance embeddings and learned pose refinement to boost robustness and visual fidelity under variable conditions.
• Block-NeRF allows flexible scene updates by retraining individual blocks, making it ideal for dynamic urban environments.

Block-NeRF: Scalable Large Scene Neural View Synthesis

The paper "Block-NeRF: Scalable Large Scene Neural View Synthesis" introduces a significant advancement in the field of neural rendering by addressing the challenges associated with large-scale environment reconstruction. The authors extend the Neural Radiance Fields (NeRF) framework to efficiently handle city-scale scenes by breaking them into manageable, independently trainable units called Block-NeRFs. This method not only enhances scalability but also allows for flexible updates, making it a practical solution for dynamic real-world environments.

Core Contributions

The primary contribution of this paper is the introduction of Block-NeRFs, a novel approach that decomposes large environments into smaller, manageable neural radiance fields. Each block can be trained independently, allowing for scalable and parallelized processing, essential for handling extensive datasets comprising millions of images.

1. Model Improvements: The authors integrate appearance embeddings, learned pose refinement, and controllable exposure adjustments. These modifications improve NeRF's robustness in handling data captured under varying conditions, such as different weather or lighting scenarios, enhancing the visual fidelity of the rendered scenes.
2. Scalability: By decoupling rendering from scene size, the Block-NeRF framework enables rendering at any required scale without a loss in computational efficiency. This approach facilitates rendering city-scale environments without the need for a monolithic model, which traditionally suffers from memory and processing bottlenecks.
3. Flexibility in Scene Updating: The ability to update individual blocks without retraining the entire model is particularly beneficial for applications in dynamic environments, where changes such as construction or modifications in surroundings are frequent. This feature is showcased in the reconstruction of the Alamo Square neighborhood in San Francisco.
4. Alignment and Compositing: The paper introduces a procedure for aligning the appearance of adjacent Block-NeRFs to ensure consistent visual quality across the entire reconstructed scene. This is achieved through an appearance matching technique that optimizes the appearance embeddings.

Experimental Validation

The authors validate their approach using extensive datasets collected over multiple months and demonstrate the Block-NeRF's ability to handle large-scale urban environments. They employ a variety of metrics, including PSNR, SSIM, and LPIPS, to objectively evaluate the performance improvements brought by their method.

Implications and Future Directions

The implications of Block-NeRF are substantial for fields such as autonomous driving, urban mapping, and robotic simulation. The ability to generate high-fidelity maps quickly and update them efficiently presents new opportunities for improving localization and navigation systems. Furthermore, the improved capacity to handle variable conditions and transient objects opens avenues for more realistic and adaptable simulations.

Future research could focus on further optimizing Block-NeRFs to reduce computational demands and enhance real-time rendering capabilities. Exploring integration with dynamic object modeling and improving handling of transient scene elements, such as moving vehicles and pedestrians, could also yield considerable advancements.

In conclusion, the Block-NeRF framework represents a practical and scalable approach to neural scene synthesis in large environments, with potential applications in multiple domains requiring detailed and adaptive environmental representations.