CombiNeRF: A Combination of Regularization Techniques for Few-Shot Neural Radiance Field View Synthesis

CombiNeRF: Enhancing Few-Shot View Synthesis with Composite Regularization Techniques

Introduction to CombiNeRF

In an attempt to address the challenges of few-shot Neural Radiance Field (NeRF) based novel view synthesis, Bonotto et al. introduce CombiNeRF, a sophisticated framework that synergistically combines multiple regularization approaches. This model is designed to enhance the generalization capacity of NeRFs especially in scenarios where only a limited number of input views are available. The central thesis of CombiNeRF is to exploit the benefits of various regularization techniques, both novel and existing ones, by integrating them within a unified framework. This integration not only preserves the geometric and chromatic consistency of the generated renderings but also significantly outperforms state-of-the-art methods across several benchmarks.

Key Contributions of the Study

The primary contributions of the paper are as follows:

1. A modification to the KL-Divergence approach originally proposed in InfoNeRF is introduced, emphasizing the regulation of neighboring rays' distributions alongside a smoothness term to regularize adjacent geometries.
2. The paper pioneers the imposition of Lipschitz regularization across all layers of both density and color networks in a NeRF, articulating noticeable enhancements in reconstruction and rendering quality.
3. CombiNeRF framework is presented, showcasing a combination of regularization losses tailored specifically for improving few-shot learning settings.
4. Through extensive evaluations on publicly accessible datasets, CombiNeRF establishes new state-of-the-art benchmarks for few-shot view synthesis.
5. An open-source release of the CombiNeRF implementation, encouraging further research and reproducibility.

Techniques Combined in CombiNeRF

The novel framework integrates a variety of regularization techniques detailed below:

• KL-Divergence and Smoothness Regularization: A new formulation to regularize the distribution of both single and neighboring rays, adding a smoothness term for near geometries.
• Lipschitz Regularization: Applied directly to the network weights, this technique enforces the smoothness of network output changes based on input alterations.
• Encoding Mask for High-Frequency Components: An input mask progressively filters high-frequency components during early training stages, allowing the network to focus on robust low-frequency information initially.
• Composite Regularization Losses: Includes the modified KL-Divergence loss, a distortion and full geometry loss for mitigating artifacts, and a depth smoothness loss for encouraging surface smoothness.

Experimental Validation and Insights

CombiNeRF was rigorously tested on the LLFF and the NeRF-Synthetic datasets under few-shot settings. Across these evaluations, CombiNeRF consistently outperformed existing methods, affirming its effectiveness and efficiency in few-shot view synthesis. An ablation study further dissected the contributions of individual components within CombiNeRF, providing insights into their relative importance and synergistic effects.

Future Directions and Conclusion

This study heralds a significant advancement in few-shot Neural Radiance Fields by meticulously combining various regularization strategies within a single, comprehensive framework. CombiNeRF not only sets new performance benchmarks but also opens avenues for further explorations into the optimization and application of regularization techniques within NeRFs. Future work could delve into exploring additional regularization strategies, refining existing ones for even better performance, and extending the application of the CombiNeRF framework to more challenging and diverse datasets.