Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification (2203.10192v1)

Abstract: A critical limitation of current methods based on Neural Radiance Fields (NeRF) is that they are unable to quantify the uncertainty associated with the learned appearance and geometry of the scene. This information is paramount in real applications such as medical diagnosis or autonomous driving where, to reduce potentially catastrophic failures, the confidence on the model outputs must be included into the decision-making process. In this context, we introduce Conditional-Flow NeRF (CF-NeRF), a novel probabilistic framework to incorporate uncertainty quantification into NeRF-based approaches. For this purpose, our method learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene. In contrast to previous approaches enforcing strong constraints over the radiance field distribution, CF-NeRF learns it in a flexible and fully data-driven manner by coupling Latent Variable Modelling and Conditional Normalizing Flows. This strategy allows to obtain reliable uncertainty estimation while preserving model expressivity. Compared to previous state-of-the-art methods proposed for uncertainty quantification in NeRF, our experiments show that the proposed method achieves significantly lower prediction errors and more reliable uncertainty values for synthetic novel view and depth-map estimation.

• The paper introduces CF-NeRF, which integrates conditional normalizing flows and latent variable modeling to accurately model radiance-density distributions with quantified uncertainty.
• The methodology achieves superior novel view synthesis and depth-map estimation performance with improved PSNR, SSIM, and LPIPS metrics compared to prior methods.
• The approach paves the way for deploying NeRF-based models in critical applications, enabling reliable decision-making by capturing uncertainty in scene representations.

Conditional-Flow NeRF: Enhancing Neural Radiance Fields with Precise Uncertainty Quantification

Introduction to Conditional-Flow NeRF

Recent advancements in 3D scene modeling have been significantly driven by the development of Neural Radiance Fields (NeRF). NeRF has shown remarkable results in synthesizing photorealistic views of complex scenes. However, a critical limitation of existing NeRF-based methods is their inability to quantify uncertainty in the learned scene representations. This gap poses a substantial challenge in critical applications like autonomous driving or medical diagnosis, where making decisions based on uncertain model outputs can lead to severe consequences.

Addressing this limitation, the paper introduces Conditional-Flow NeRF (CF-NeRF), a novel framework designed to incorporate uncertainty quantification into NeRF-based models. CF-NeRF leverages a probabilistic approach by modeling a distribution over all possible radiance fields, enabling the estimation of uncertainty in a data-driven manner. This is achieved through the utilization of Conditional Normalizing Flows (CNF) coupled with Latent Variable Modeling, significantly enhancing the model's capability to render scenes with accurate uncertainty estimates without compromising the expressivity of the model.

Key Contributions and Results

The paper details several notable contributions and experimental results:

• Modelling Radiance-Density distributions with CNF: Unlike previous methods that impose strong assumptions on the distribution of scene representations, CF-NeRF employs CNFs to learn complex distributions of radiance and density values. This approach allows CF-NeRF to model scenes with intricate geometries and appearances more accurately.
• Latent Variable Modelling for Radiance Fields: By introducing a global latent variable, CF-NeRF efficiently models the joint distribution over radiance-density variables. This strategy results in spatially-smooth uncertainty estimates and enhances the synthesized image and depth map quality.
• Quantitative and Qualitative Improvements: Compared to state-of-the-art methods, CF-NeRF demonstrates superior performance on established benchmarks. It not only achieves lower prediction errors in novel view synthesis and depth-map estimation but also yields more reliable uncertainty values, as evidenced by significant improvements in metrics such as PSNR, SSIM, and LPIPS for image quality, as well as RMSE and MAE for depth accuracy.

Implications and Future Directions

The introduction of CF-NeRF marks a significant step towards overcoming the uncertainty quantification challenge in 3D scene modeling. By combining the strengths of CNFs and latent variable modeling, CF-NeRF sets a new benchmark in synthesizing photorealistic images and depth maps with associated confidence scores. This achievement opens avenues for deploying NeRF-based models in decision-critical applications, empowering them to make informed decisions under uncertainty.

Moreover, the flexible and data-driven approach to model complex distributions of radiance fields suggests potential extensions to other variants of NeRF, catering to dynamic scenes or incorporating additional scene semantics. Exploring these avenues can further enhance the applicability and robustness of NeRF-based models across a broad spectrum of 3D scene understanding and interaction tasks.

Closing Remarks

Conditional-Flow NeRF presents a compelling solution to the critical challenge of uncertainty quantification in Neural Radiance Fields. With its ability to accurately model complex scenes and quantify associated uncertainties without sacrificing model expressivity, CF-NeRF paves the way for more reliable and informative 3D scene modeling. This work not only contributes a significant advancement to the field of 3D computer vision but also invites further research into probabilistic modeling approaches within the NeRF framework and beyond.