From NeRFs to Gaussian Splats, and Back (2405.09717v3)

Abstract: For robotics applications where there is a limited number of (typically ego-centric) views, parametric representations such as neural radiance fields (NeRFs) generalize better than non-parametric ones such as Gaussian splatting (GS) to views that are very different from those in the training data; GS however can render much faster than NeRFs. We develop a procedure to convert back and forth between the two. Our approach achieves the best of both NeRFs (superior PSNR, SSIM, and LPIPS on dissimilar views, and a compact representation) and GS (real-time rendering and ability for easily modifying the representation); the computational cost of these conversions is minor compared to training the two from scratch.

• The paper develops NeRFGS and GSNeRF methods to efficiently convert between NeRF and Gaussian Splat models, merging the benefits of implicit generalization and explicit real-time rendering.
• The paper demonstrates superior performance with higher PSNR and SSIM values compared to existing state-of-the-art methods across diverse datasets.
• The paper highlights practical applications in robotics and dynamic scene updates by enabling rapid scene editing and efficient memory utilization.

Bridging Implicit and Explicit: NeRFs to Gaussian Splats and Vice Versa

Background and Motivation

In the field of 3D scene representation, the choice between using implicit and explicit models has always involved certain trade-offs. Implicit models like Neural Radiance Fields (NeRFs) offer compact scene representation and superior generalization to new views, which is crucial for applications like robotics. On the other hand, explicit models like 3D Gaussian Splatting (GS) provide real-time rendering capabilities but often struggle with generalizing from sparse training views.

This paper introduces an efficient method to switch between these two types of representations, allowing one to leverage the strengths of both.

Key Findings

NeRFs vs. GS

While NeRFs can generalize better to novel views that were not part of the training data, GS models tend to perform well when the validation views are similar to those in the training set (see Fig. 1 in the paper). For example, when experimenting with scenes like Aspen and Giannini Hall, NeRF models demonstrated higher Peak Signal-to-Noise Ratio (PSNR) and rendered images with better depth and color accuracy at novel viewpoints.

Conversion Process: NeRF to GS

The authors developed a method called NeRFGS which initializes Gaussians in a scene based on the output of a trained NeRF model. The conversion essentially involves:

1. Rendering rays from training views to calculate scene point-clouds.
2. Initializing Gaussians at these points and fine-tuning them to improve scene capture.

Even without fine-tuning, this method showed impressive results, demonstrating the efficacy of NeRFGS in capturing geometric and photometric properties of the scene (see Fig. 2).

Conversion Process: GS to NeRF

To further capitalize on the strengths of both models, the paper introduces GSNeRF, a method that converts an explicit GS representation back to an implicit NeRF. This is particularly useful for updating the NeRF model and features distillation. They illustrate this by editing out a lamp post in a scene and updating the NeRF accordingly, which took less than 5 seconds (see Fig. 3).

Results and Performance

The paper’s experimental results show that NeRFGS can quickly and effectively approximate NeRF’s quality when rendered in real-time (see Table 1). Notably, for datasets with views that are drastically different between training and validation (like Wissahickon and Locust Walk), the NeRFGS and GSNeRF methods outperformed GS-based methods like Splatfacto and RadGS by a significant margin.

• PSNR and SSIM values were generally higher for NeRFGS and GSNeRF across various datasets.
• Real-Time Rendering was achieved with GS models, crucial for applications needing immediate scene understanding, like robotic navigation.

Implications

Practical Benefits

1. Real-Time Rendering: GS models' quick rendering is valuable for tasks requiring immediate scene updates, such as localization and planning in robotic systems.
2. Efficient Scene Updates: GSNeRF allows for quick modifications to the scene, something typically laborious with static NeRFs.
3. Memory Efficiency: NeRFs require less memory, making them suitable for resource-constrained devices.

Theoretical Insights

1. Dual Architecture Utility: The ability to switch representations dynamically offers a hybrid approach, optimizing rendering speed and memory efficiency.
2. Generalization Capabilities: This approach suggests that implicit models can be distilled into explicit ones without significant loss in scene fidelity, up to a point.
3. Future Research Directions: There is room to refine the conversion process between NeRF and GS to minimize inefficiencies and further improve quality metrics like PSNR and SSIM.

Future Developments

The approach presented sets a foundation for future enhancements. Here are a few speculative areas for development:

1. Optimized Conversion Methods: Reducing inefficiencies in the NeRF to GS conversion process to boost initial PSNR values.
2. Dynamic Scene Modeling: Applying these hybrid models in dynamic environments with changing objects could advance real-time adaptability.
3. Feature Integration: Combining these methods with other advanced AI algorithms could lead to more robust and versatile 3D scene representations.

The methods and results discussed in this paper open a pathway to more adaptable and efficient 3D modeling techniques, potentially driving advancements in various AI and robotics applications. This ability to leverage both implicit and explicit methodologies offers a promising direction for enhancing the capabilities of automated systems.