GP-GS: Gaussian Processes for Enhanced Gaussian Splatting (2502.02283v5)

Abstract: 3D Gaussian Splatting has emerged as an efficient photorealistic novel view synthesis method. However, its reliance on sparse Structure-from-Motion (SfM) point clouds often limits scene reconstruction quality. To address the limitation, this paper proposes a novel 3D reconstruction framework, Gaussian Processes enhanced Gaussian Splatting (GP-GS), in which a multi-output Gaussian Process model is developed to enable adaptive and uncertainty-guided densification of sparse SfM point clouds. Specifically, we propose a dynamic sampling and filtering pipeline that adaptively expands the SfM point clouds by leveraging GP-based predictions to infer new candidate points from the input 2D pixels and depth maps. The pipeline utilizes uncertainty estimates to guide the pruning of high-variance predictions, ensuring geometric consistency and enabling the generation of dense point clouds. These densified point clouds provide high-quality initial 3D Gaussians, enhancing reconstruction performance. Extensive experiments conducted on synthetic and real-world datasets across various scales validate the effectiveness and practicality of the proposed framework.

• The paper introduces a Multi-Output Gaussian Process model to enrich sparse SfM point clouds, significantly improving Gaussian Splatting based 3D reconstructions.
• It employs an adaptive sampling and uncertainty-based filtering strategy to optimize point density and accurately capture scene structures.
• Experimental results across benchmarks show substantial gains in PSNR, SSIM, and LPIPS metrics, enhancing the photorealism of rendered scenes.

Overview of GP-GS: Gaussian Processes for Enhanced Gaussian Splatting

The paper "GP-GS: Gaussian Processes for Enhanced Gaussian Splatting" presents a novel framework aimed at improving the quality of 3D reconstruction in the context of Gaussian Splatting, a method utilized for photorealistic novel view synthesis. The authors introduce Gaussian Processes (GPs) into the Gaussian Splatting (3DGS) pipeline to address the limitations posed by sparse point clouds typically obtained from Structure-from-Motion (SfM) techniques, which often lead to incomplete scene reconstructions.

Background and Motivation

Gaussian Splatting has been recognized for its efficiency in rendering novel views by leveraging a set of 3D Gaussians, generated from points detected through SfM processes. However, due to the inherent sparsity in SfM, the resulting point clouds can often be inadequate, particularly in texture-less or highly cluttered regions. This shortfall significantly affects the initial placement and density of the Gaussians, leading to subpar rendering quality and geometric fidelity. Therefore, the authors propose enhancing the input data with a density-enriched point cloud, guided by the predictions of a Multi-Output Gaussian Process (MOGP).

Methodological Contributions

1. Multi-Output Gaussian Process Model: The paper proposes using MOGP to adaptively densify sparse point clouds derived from SfM. This model is trained to predict denser point clouds by learning the mapping from 2D image pixels, augmented by depth priors, to a three-dimensional space.
2. Adaptive Sampling and Filtering Strategy: The authors have developed a neighborhood-based sampling technique that dynamically selects pixels as candidates for the Gaussian Processes to perform predictions. The approach includes uncertainty-based filtering to prune predictions with high variance, ensuring the remaining densified points adhere closely to the actual scene structure.
3. Integration with Existing Tools: GP-GS is designed as a plug-and-play module, making it highly compatible with existing SfM-based workflows for 3D rendering enhancement, thus offering a flexible improvement that can be easily incorporated into current systems.

Results and Implications

Quantitative assessment across several benchmark datasets, such as NeRF Synthetic, Mip-NeRF 360, and Tanks and Temples, shows significant improvements in rendering quality metrics, including PSNR, SSIM, and LPIPS, when using GP-GS compared to traditional 3DGS*. The paper reports substantial gains in precision, particularly noteworthy in regions of high complexity, such as foliage and poorly lit environments.

The methodological advancements presented in the paper have profound implications. From a practical standpoint, GP-GS enhances the reality capture capabilities of 3D rendering applications, broadening its applicability in fields like virtual reality and robotics, where high-fidelity reconstructions are crucial. Theoretically, the incorporation of Gaussian Processes highlights a novel application of statistical machine learning models to improve the geometric precision of reconstructed scenes, setting a precedent for further exploration of these models in solving similar densification problems.

Future Directions

The paper opens up avenues for extending the GP-GS framework to dynamic scenes, leveraging temporal information to update point cloud densities in real-time, potentially revolutionizing domains requiring rapid and accurate environmental mapping. Additionally, further refinement of the Gaussian Process model, perhaps through exploring more sophisticated kernels or utilizing neural network-inspired structures, could yield even better performance and generalization.

In conclusion, the paper offers an insightful exploration into the integration of Gaussian Processes with Gaussian Splatting, marking a significant step forward in the pursuit of high-quality 3D scene reconstruction and rendering. The innovations it introduces are poised to influence both current practices and future research in the field.