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Modeling uncertainty for Gaussian Splatting (2403.18476v1)

Published 27 Mar 2024 in cs.CV and cs.GR

Abstract: We present Stochastic Gaussian Splatting (SGS): the first framework for uncertainty estimation using Gaussian Splatting (GS). GS recently advanced the novel-view synthesis field by achieving impressive reconstruction quality at a fraction of the computational cost of Neural Radiance Fields (NeRF). However, contrary to the latter, it still lacks the ability to provide information about the confidence associated with their outputs. To address this limitation, in this paper, we introduce a Variational Inference-based approach that seamlessly integrates uncertainty prediction into the common rendering pipeline of GS. Additionally, we introduce the Area Under Sparsification Error (AUSE) as a new term in the loss function, enabling optimization of uncertainty estimation alongside image reconstruction. Experimental results on the LLFF dataset demonstrate that our method outperforms existing approaches in terms of both image rendering quality and uncertainty estimation accuracy. Overall, our framework equips practitioners with valuable insights into the reliability of synthesized views, facilitating safer decision-making in real-world applications.

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Citations (1)
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Summary

  • The paper introduces Stochastic Gaussian Splatting (SGS), a novel framework that integrates uncertainty prediction into efficient Gaussian Splatting.
  • The method leverages a Bayesian framework with Variational Inference and augments accuracy using the Area Under Sparsification Error (AUSE) metric.
  • Experimental results on the LLFF dataset show that SGS improves both rendering quality and uncertainty estimation compared to NeRF-based approaches.

Stochastic Gaussian Splatting: A Novel Framework for Uncertainty Estimation in Gaussian Splatting

Introduction

Novel-view synthesis stands as a significant challenge within the domain of computer vision, boasting applications that span across virtual reality, augmented reality, and robotics. Pioneering advancements, most notably Neural Radiance Fields (NeRF), have shifted the paradigm of scene representation enabling high-fidelity view synthesis. Albeit impressive, NeRF's computational and memory demands hamper its application in real-time scenarios. Emerging as a computationally efficient alternative, Gaussian Splatting (GS) has shown promising results, albeit with a notable limitation: the absence of uncertainty estimation in synthesized views. Addressing this gap, we propose Stochastic Gaussian Splatting (SGS), the first framework that integrates uncertainty prediction within GS, leveraging Variational Inference (VI) and introducing the Area Under Sparsification Error (AUSE) within the optimization process.

Methodology

Stochastic Gaussian Splatting (SGS)

SGS is an extension of GS, incorporating uncertainty prediction into the rendering pipeline. The method achieves this by transforming GS parameters into random variables with defined prior distributions, introducing stochasticity into the model. This approach not only retains GS's real-time rendering capability but also facilitates uncertainty estimation.

Bayesian Framework with Variational Inference

Adopting a Bayesian standpoint, we utilize Variational Inference to estimate the uncertainty. Here, the parameters of GS are learned through VI, allowing for a seamless integration of uncertainty prediction. The method capitalizes on GS's efficiency, enabling multiple samplings within a single forward pass, which is utilized to approximate the variance of pixel colors, thus assessing uncertainty directly.

Augmentation with AUSE

To hone the accuracy of uncertainty estimation, we propose augmenting the VI framework with the AUSE metric. This novel addition aims to optimize the correlation between uncertainty predictions and actual error, thus enhancing the model's reliability.

Experimental Evaluation

Dataset and Setup

We conducted experiments on the LLFF dataset, comparing our method against existing approaches that largely rely on the more computationally intensive NeRF. SGS demonstrates superior performance in both image rendering quality and uncertainty estimation accuracy, making a strong case for its application in real-world scenarios.

Results

Our findings indicate a significant leap in rendering quality alongside more accurate uncertainty predictions when benchmarked against state-of-the-art methods. This outcome underscores the efficacy of SGS and its potential in reshaping the landscape of novel-view synthesis, providing a computationally efficient pathway that does not forgo the assessment of uncertainty.

Implications and Future Work

The introduction of SGS marks a significant stride in bridging the gap between computational efficiency and uncertainty estimation in novel-view synthesis. The framework not only aids in enhancing the understanding and reliability of synthesized views but also sets a precedent for future explorations in the domain. Looking ahead, this research opens up avenues for further advancements, particularly in the optimization of the proposed Bayesian framework and the exploration of its applicability across varying domains and tasks in computer vision.

Conclusion

In conclusion, Stochastic Gaussian Splatting stands as a pioneering framework that integrates uncertainty estimation into Gaussian Splatting, upholding computational efficiency while offering detailed insights into the reliability of synthesized views. Its performance on the LLFF dataset underscores its potential in revolutionizing novel-view synthesis, fostering developments that could greatly benefit real-world applications across various fields.