S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting (2503.04314v1)

Abstract: In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and clarity. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and complicated scenarios. To this end, we propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views. The S2Gaussian operates in a two-stage fashion. In the first stage, we initially optimize a low-resolution Gaussian representation with depth regularization and densify it to initialize the high-resolution Gaussians through a tailored Gaussian Shuffle Split operation. In the second stage, we refine the high-resolution Gaussians with the super-resolved images generated from both original sparse views and pseudo-views rendered by the low-resolution Gaussians. In which a customized blur-free inconsistency modeling scheme and a 3D robust optimization strategy are elaborately designed to mitigate multi-view inconsistency and eliminate erroneous updates caused by imperfect supervision. Extensive experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details.

• The paper introduces S2Gaussian, a novel two-stage framework for high-quality 3D reconstruction from sparse, low-resolution views using techniques like Gaussian Shuffle Split and optimized high-resolution Gaussian constructs.
• Quantitative evaluations demonstrate that S2Gaussian achieves state-of-the-art performance on multiple benchmarks, significantly improving fidelity metrics like PSNR, SSIM, and LPIPS compared to existing methods.
• The framework's ability to reconstruct detailed 3D scenes from limited input has practical implications for applications requiring photorealistic rendering in virtual reality and metaverse environments.

Sparse-View Super-Resolution 3D Gaussian Splatting Framework

The paper presents S2Gaussian, a novel framework for the high-quality reconstruction of 3D scenes from sparse low-resolution input views. It addresses a unique challenge that combines the hurdles of sparse view reconstruction and low-resolution super-resolution, which earlier methods have typically handled independently. By employing a two-stage operation, S2Gaussian achieves superior fidelity in reconstructing detailed 3D scenes, making it particularly practical for realistic applications such as virtual reality and metaverse technologies.

In the first stage, S2Gaussian initializes a low-resolution Gaussian representation, which is subsequently enhanced through a dense Gaussian Shuffle Split operation. This method optimizes for depth regularization, which transforms sparse viewpoints into a more comprehensive high-resolution Gaussian structure. The Shuffle Split operation in particular is critical as it divides each Gaussian primitive into multiple detailed components, facilitating an intricate and accurate reconstruction of fine-grained details.

The second stage optimizes the high-resolution Gaussian constructs using super-resolved images derived through both the original sparse views and additional synthesized pseudo-views. This stage is augmented by a blur-free inconsistency modeling scheme and a 3D robust optimization strategy, which effectively address the multi-view inconsistencies and erroneous updates that can otherwise degrade the accuracy and detail in the final 3D representation. By incorporating these methodologies, S2Gaussian achieves new state-of-the-art performance across various benchmarks.

Quantitative evaluations of S2Gaussian demonstrate its efficacy, with notable improvements across metrics such as PSNR, SSIM, LPIPS, and FID on datasets like Blender, LLFF, and Mip-NeRF 360. The proposed framework consistently outperformed existing methods, with significant quantitative and visual enhancements in the fidelity of reconstructed scenes. This work demonstrates a systematic approach to tackling both sparsity and resolution deficiency in 3D scene reconstruction, advancing the theoretical understanding and practical deployment of 3D Gaussian splatting techniques in real-world applications.

Theoretical implications of this work are substantial. By integrating Gaussian Shuffle Splits and novel optimizations, this paper paves the way for future research in improving computational graphics and scene reconstruction. Further investigation could explore the limits of pseudo-view generation or adapt similar model architectures to address other challenging scenarios in 3D modeling and rendering. This progression could unlock new dimensions in visualization technology, closely aligned with practical demands for photorealism in increasingly complex and dynamic virtual scenes.