LoopSparseGS: Loop Based Sparse-View Friendly Gaussian Splatting
(2408.00254)Abstract
Despite the photorealistic novel view synthesis (NVS) performance achieved by the original 3D Gaussian splatting (3DGS), its rendering quality significantly degrades with sparse input views. This performance drop is mainly caused by the limited number of initial points generated from the sparse input, insufficient supervision during the training process, and inadequate regularization of the oversized Gaussian ellipsoids. To handle these issues, we propose the LoopSparseGS, a loop-based 3DGS framework for the sparse novel view synthesis task. In specific, we propose a loop-based Progressive Gaussian Initialization (PGI) strategy that could iteratively densify the initialized point cloud using the rendered pseudo images during the training process. Then, the sparse and reliable depth from the Structure from Motion, and the window-based dense monocular depth are leveraged to provide precise geometric supervision via the proposed Depth-alignment Regularization (DAR). Additionally, we introduce a novel Sparse-friendly Sampling (SFS) strategy to handle oversized Gaussian ellipsoids leading to large pixel errors. Comprehensive experiments on four datasets demonstrate that LoopSparseGS outperforms existing state-of-the-art methods for sparse-input novel view synthesis, across indoor, outdoor, and object-level scenes with various image resolutions.
Overview
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LoopSparseGS is a novel framework designed to improve the accuracy of novel view synthesis with sparse input data through strategies like Progressive Gaussian Initialization (PGI), Depth-alignment Regularization (DAR), and Sparse-friendly Sampling (SFS).
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Experimental results on various datasets demonstrate that LoopSparseGS outperforms existing methods in metrics such as PSNR, SSIM, and LPIPS, showcasing its ability to produce photorealistic images with finer details.
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The framework's innovative approach, including iterative densification and alignment mechanisms, presents significant advancements for applications in robotics, augmented reality, and broadcasting, with future potential for integration with advanced depth estimation techniques and dynamic scenes.
LoopSparseGS: Enhancing Sparse-Input Novel View Synthesis Using Gaussian Splatting
The paper "LoopSparseGS: Loop Based Sparse-View Friendly Gaussian Splatting" presents a novel framework aimed at addressing the challenges in sparse-input novel view synthesis. This framework, termed LoopSparseGS, extends the capabilities of the existing 3D Gaussian Splatting (3DGS) method to handle scenarios where only a limited number of input views are available. The framework is built upon three key strategies: Progressive Gaussian Initialization (PGI), Depth-alignment Regularization (DAR), and Sparse-friendly Sampling (SFS).
Technical Contributions and Methodology
Progressive Gaussian Initialization (PGI)
The PGI strategy leverages a looping mechanism to iteratively improve the initialization of Gaussian points. By incorporating rendered pseudo-images with the original training images, PGI densifies the initial point cloud, resulting in more comprehensive scene coverage. This densification is achieved by generating new pseudo-views around the training views and integrating these pseudo images into the training process in successive loops. This iterative process allows the model to progressively refine the initialized Gaussian points, leading to improved training convergence and scene representation.
Depth-alignment Regularization (DAR)
DAR addresses the geometric constraints in the optimization process by combining sparse depth information obtained from Structure from Motion (SfM) with dense monocular depth cues. A sliding window-based Pearson correlation loss aligns the absolute depth constraints provided by SfM with the relative depth constraints from monocular depth maps. This alignment mitigates the scale inconsistency issues inherent in monocular depth estimation and enhances the overall geometric fidelity of the rendered scenes.
Sparse-friendly Sampling (SFS)
To tackle the problem of oversized Gaussian ellipsoids that can arise from sparse input views, the SFS strategy selectively splits these large ellipsoids based on pixel error metrics. By identifying ellipsoids associated with high-error pixels and subdividing them, SFS improves the representation of fine details in the scene. This targeted densification helps in maintaining the rendering quality without significantly increasing the overall computation requirements.
Experimental Validation and Results
The authors validate their approach through extensive experiments on four datasets: LLFF, DTU, Mip-NeRF360, and Blender. The results consistently demonstrate that LoopSparseGS outperforms existing state-of-the-art methods across various metrics, including PSNR, SSIM, and LPIPS. For instance, on the LLFF dataset, the proposed method achieves significant improvements in PSNR over other methods, particularly at lower input resolutions. Qualitative results further highlight the ability of LoopSparseGS to produce photorealistic images with finer details and fewer artifacts compared to competing methods.
Implications and Future Directions
Practically, LoopSparseGS offers a robust solution for applications requiring photo-realistic image synthesis from sparse views, such as in robotics, augmented reality, and broadcasting. Theoretically, the introduction of iterative densification and alignment mechanisms opens new avenues for improving the initialization and optimization of 3D representations in novel view synthesis tasks.
Future developments could focus on enhancing the efficiency and scalability of the framework. Integrating LoopSparseGS with more advanced depth estimation techniques, or exploring its applicability in dynamic scenes, could further elevate its performance and adaptability. Additionally, investigating how LoopSparseGS interacts with other types of neural representations and rasterization techniques may provide broader insights into its potential utility across diverse applications in computer vision and graphics.
In conclusion, LoopSparseGS successfully mitigates the limitations of traditional Gaussian splatting techniques when dealing with sparse input data. Its carefully designed strategies for initialization, regularization, and sampling significantly enhance both the quality and robustness of novel view synthesis, marking a valuable contribution to the field.
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