Improving Gaussian Splatting with Localized Points Management (2406.04251v3)

Abstract: Point management is critical for optimizing 3D Gaussian Splatting models, as point initiation (e.g., via structure from motion) is often distributionally inappropriate. Typically, Adaptive Density Control (ADC) algorithm is adopted, leveraging view-averaged gradient magnitude thresholding for point densification, opacity thresholding for pruning, and regular all-points opacity reset. We reveal that this strategy is limited in tackling intricate/special image regions (e.g., transparent) due to inability of identifying all 3D zones requiring point densification, and lacking an appropriate mechanism to handle ill-conditioned points with negative impacts (e.g., occlusion due to false high opacity). To address these limitations, we propose a Localized Point Management (LPM) strategy, capable of identifying those error-contributing zones in greatest need for both point addition and geometry calibration. Zone identification is achieved by leveraging the underlying multiview geometry constraints, subject to image rendering errors. We apply point densification in the identified zones and then reset the opacity of the points in front of these regions, creating a new opportunity to correct poorly conditioned points. Serving as a versatile plugin, LPM can be seamlessly integrated into existing static 3D and dynamic 4D Gaussian Splatting models with minimal additional cost. Experimental evaluations validate the efficacy of our LPM in boosting a variety of existing 3D/4D models both quantitatively and qualitatively. Notably, LPM improves both static 3DGS and dynamic SpaceTimeGS to achieve state-of-the-art rendering quality while retaining real-time speeds, excelling on challenging datasets such as Tanks & Temples and the Neural 3D Video dataset.

• The paper introduces a localized point management strategy that uses error maps and multiview constraints to precisely identify and optimize under-optimized 3D areas.
• The methodology densifies error-prone regions and recalibrates point opacity, resulting in significant improvements in PSNR, SSIM, and LPIPS benchmarks.
• The LPM approach enhances rendering quality while maintaining computational efficiency, making it well-suited for real-time applications in VR, AR, and robotics.

Localized Gaussian Point Management

This paper introduces a novel point management strategy, Localized Point Management (LPM), to address the inherent limitations of Adaptive Density Control (ADC) in the optimization of 3D Gaussian Splatting (3DGS) models. This approach is particularly relevant for enhancing the rendering quality and computational efficiency in both static 3D and dynamic 4D scenes.

Introduction

Neural rendering has gained prominence as a flexible and powerful methodology for photorealistic novel view synthesis (NVS). Approaches like Neural Radiance Fields (NeRF) employ implicit scene representations using neural networks, achieving high-quality results at the cost of significant computational resources. Conversely, 3D Gaussian Splatting (3DGS) provides a more efficient alternative, leveraging explicit Gaussian point representations initialized via Structure from Motion (SfM) algorithms. Despite its advantages, 3DGS necessitates a robust point management mechanism—typically ADC—to manage point distribution during optimization. However, ADC's simplistic view-averaged gradient magnitude thresholding often fails to adequately address under-optimized points, particularly in complex image regions.

Methodology

LPM aims to enhance the point management process by introducing a localized approach that identifies error-contributing zones for targeted point addition and geometry calibration. This process is guided by image rendering errors and multiview geometry constraints.

Error Map Generation

An initial error map is generated by comparing rendered images against ground-truth images, serving as the basis for identifying regions requiring optimization.

Error Contributing 3D Zone Identification

LPM capitalizes on multiview geometry constraints to project 2D image errors back into the 3D space. This involves feature mapping to identify corresponding regions in different views, followed by ray casting to establish error source zones in 3D space.

Points Manipulation

Within the identified error zones, LPM performs localized point addition and geometry calibration. This involves densifying under-optimized areas and resetting the opacity of ill-conditioned points to correct potential rendering inaccuracies. This localized approach allows for more precise adjustments without the computational overhead associated with global threshold adjustments.

Experimental Validation

The efficacy of LPM was validated through extensive experiments on both static 3D datasets (Mip-NeRF360, Tanks and Temples, DeepBlending) and dynamic 4D datasets (Neural 3D Video Dataset). Across these benchmarks, LPM consistently improved the rendering quality of baseline 3DGS models.

Static 3D Results

Quantitative metrics (PSNR, SSIM, LPIPS) indicate that LPM enhances the performance of 3DGS on various challenging scenarios, such as light effects, intricate details, and transparency. Notably, LPM achieves comparable results to state-of-the-art methods on the Mip-NeRF360 dataset and sets new benchmarks on Tanks and Temples and DeepBlending datasets.

Dynamic 4D Results

LPM also shows significant improvements on dynamic scenes, especially in regions with both static and dynamic movements. The approach outperforms the baseline SpaceTimeGS (STGS) across all tested scenes, retaining real-time rendering speeds while achieving higher fidelity in novel view synthesis.

Implications and Future Work

The proposed LPM strategy significantly enhances the granularity and accuracy of point management in 3DGS models. This localized approach not only improves rendering quality but also maintains computational efficiency, making it highly suitable for real-time applications. Future research could explore optimizing the point addition rules further and automating the feature mapping process for even better performance in diverse and complex scenes.

Conclusion

Localized Gaussian Point Management offers a robust solution to the limitations of conventional ADC in 3D Gaussian Splatting. By focusing on localized point densification and geometry calibration guided by rendering errors, LPM substantially improves both the quantitative and qualitative aspects of 3DGS-based rendering models. This advancement holds significant potential for applications in virtual and augmented reality, robotics, and high-fidelity content generation.