Self-Evolving Depth-Supervised 3D Gaussian Splatting from Rendered Stereo Pairs (2409.07456v1)

Abstract: 3D Gaussian Splatting (GS) significantly struggles to accurately represent the underlying 3D scene geometry, resulting in inaccuracies and floating artifacts when rendering depth maps. In this paper, we address this limitation, undertaking a comprehensive analysis of the integration of depth priors throughout the optimization process of Gaussian primitives, and present a novel strategy for this purpose. This latter dynamically exploits depth cues from a readily available stereo network, processing virtual stereo pairs rendered by the GS model itself during training and achieving consistent self-improvement of the scene representation. Experimental results on three popular datasets, breaking ground as the first to assess depth accuracy for these models, validate our findings.

• The paper introduces a novel self-evolving approach that integrates stereo matching depth priors to significantly improve 3D scene geometry in Gaussian Splatting.
• It leverages rendered stereo pairs processed by a deep stereo network to dynamically refine depth maps during training.
• Experimental evaluations on ETH3D, ScanNet++, and BlendedMVS demonstrate marked improvements in depth accuracy and photorealistic rendering.

Self-Evolving Depth-Supervised 3D Gaussian Splatting from Rendered Stereo Pairs

The paper, "Self-Evolving Depth-Supervised 3D Gaussian Splatting from Rendered Stereo Pairs" by Safadoust et al., focuses on addressing a critical limitation inherent in 3D Gaussian Splatting (GS). GS, while achieving notable advancements in photorealism and rendering speed, suffers from inaccuracies in representing the underlying 3D scene geometry. This limitation manifests in visual artifacts within the depth maps generated by GS. The authors propose a novel approach that dynamically exploits depth cues and integrates depth priors within the optimization process to significantly enhance the accuracy and realism of these depth maps.

Motivation and Challenges

The limitations of GS are intrinsically linked to the geometry it models, which lacks the accuracy and consistency reflected in the rendered images. Previous works have explored the use of depth priors to improve image rendering in the context of NeRF, but have not extended this focus to GS. The authors identify an opportunity to leverage depth priors more effectively by incorporating them into GS optimization. Specifically, they propose generating these priors from virtual stereo pairs rendered by the GS model, processed by a deep stereo network, thus enabling the GS to continuously self-improve during training.

Methodology

The authors begin by reviewing four main strategies for extracting depth priors from the images used in GS optimization:

1. Structure-from-Motion (SfM)
2. Monocular Depth Estimation (MDE)
3. Depth Completion (DC)
4. Multi-View Stereo (MVS)

Each method offers unique strengths and limitations. For example, while SfM and MVS require overlapping image views for accurate point matching, MDE and DC are not restricted by this requirement but demand robust network generalization across different scenes. However, the core contribution of this work lies in the introduction of a fifth strategy: stereo matching.

Self-Evolving Depth-Supervised 3D Gaussian Splatting

The self-evolving GS framework introduced in this paper capitalizes on the consistent geometric rendering capability of GS despite its initially inaccurate geometry. By rendering rectified stereo image pairs during training, the method employs a pre-trained deep stereo network to extract supplementary depth priors. The incorporation of these depth priors into the GS optimization process fosters continuous improvements in both depth map accuracy and overall visual quality.

Experimental Evaluation

The proposed approach is rigorously evaluated against other depth-from-image solutions using three datasets: ETH3D, ScanNet++, and BlendedMVS. Key results include:

• ETH3D: The proposed self-evolving GS framework outperforms other methods in terms of depth estimation accuracy (Abs. Rel. 0.057) and rendering quality (SSIM 0.7704, PSNR 22.2825).
• ScanNet++: The framework achieves significant improvements (Abs. Rel. 0.068, SSIM 0.9165, PSNR 28.1488), highlighting its robustness across diverse scenes.
• BlendedMVS: The method excels in depth accuracy (Abs. Rel. 0.020) and shows competitive rendering results (SSIM 0.6377, PSNR 21.9734), validating its effectiveness on semi-synthetic datasets.

Implications and Future Work

The implications of this work span both practical and theoretical domains in AI and computer vision:

• Practical: The self-evolving GS framework sets a new standard for rendering quality and depth map accuracy, suggesting wide-ranging applications in AR/VR, 3D reconstruction, and autonomous navigation.
• Theoretical: The integration of dynamically generated depth priors within GS optimization paves the way for further research into self-improving models and the use of auxiliary data streams in neural rendering.

Future research should explore extending the self-evolving framework to other neural rendering techniques, investigating alternative depth estimation methods, and optimizing the computational efficiency of depth prior generation and usage.

Conclusion

Safadoust et al. present a compelling case for enhancing 3D Gaussian Splatting through self-evolving depth supervision from virtual stereo pairs. Their method demonstrates significant gains in depth map accuracy and rendering quality, validated across diverse datasets. This contribution not only addresses a critical shortcoming in existing GS methodologies but also opens avenues for future exploration in dynamic optimization and neural rendering.