Improving Gaussian Splatting with Localized Points Management

2024-06-06

AI Summary

Existing adaptive density control (ADC) in 3D Gaussian Splatting (3DGS) fails in transparent/occluded regions, struggles to precisely localize 3D regions requiring optimization, and cannot rectify pathological points (e.g., spurious occlusions caused by excessively high opacity). To address these issues, we propose Localized Point Management (LPM), a novel paradigm that jointly leverages multi-view geometric consistency and pixel-level rendering error to accurately localize error-sensitive 3D regions. LPM dynamically optimizes point distribution via local gradient-driven densification and opacity reset of foreground-high-opacity points. Implemented as a lightweight plug-in, it requires no modification to the core training pipeline. On Tanks & Temples and Neural 3D Video benchmarks, LPM achieves state-of-the-art performance for both static 3DGS and dynamic SpaceTimeGS—significantly improving PSNR, SSIM, and LPIPS—while preserving real-time inference speed and incurring negligible computational overhead.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Local Search
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Data transparency and provenance

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.

Problem

• Optimizing point management in 3D Gaussian Splatting models
• Handling intricate image regions like transparent areas
• Correcting ill-conditioned points causing occlusion errors

Innovation

• Localized Point Management for error-contributing zones
• Multiview geometry constraints for zone identification
• Opacity reset in front of problematic regions