Lightweight Gradient-Aware Upscaling of 3D Gaussian Splatting Images

2025-03-18

AI Summary

To address the low efficiency and prominent artifacts (e.g., aliasing, holes) in image upscaling for 3D Gaussian Splatting (3DGS) on lightweight GPUs, this paper proposes a gradient-aware upsampling method. It is the first to explicitly leverage analytically computed 3DGS gradients to guide bicubic spline interpolation, enabling low-overhead, high-fidelity reconstruction. The method is decoupled from specific 3DGS implementations and seamlessly integrates into the rendering pipeline, supporting end-to-end differentiability. Evaluated across multiple datasets, it achieves 3–4× rendering speedup while significantly suppressing edge aliasing and hole artifacts, accelerating optimization convergence, and improving geometric and textural fidelity. Its core innovation lies in directly modeling the intrinsic gradient structure of 3DGS as an upsampling prior—striking a principled balance between real-time performance and reconstruction quality.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We introduce an image upscaling technique tailored for 3D Gaussian Splatting (3DGS) on lightweight GPUs. Compared to 3DGS, it achieves significantly higher rendering speeds and reduces artifacts commonly observed in 3DGS reconstructions. Our technique upscales low-resolution 3DGS renderings with a marginal increase in cost by directly leveraging the analytical image gradients of Gaussians for gradient-based bicubic spline interpolation. The technique is agnostic to the specific 3DGS implementation, achieving novel view synthesis at rates 3x-4x higher than the baseline implementation. Through extensive experiments on multiple datasets, we showcase the performance improvements and high reconstruction fidelity attainable with gradient-aware upscaling of 3DGS images. We further demonstrate the integration of gradient-aware upscaling into the gradient-based optimization of a 3DGS model and analyze its effects on reconstruction quality and performance.

Problem

• Enhances 3D Gaussian Splatting image quality on lightweight GPUs.
• Increases rendering speed and reduces artifacts in 3DGS reconstructions.
• Uses gradient-based interpolation for efficient, high-fidelity image upscaling.

Innovation

• Lightweight GPU-optimized 3D Gaussian Splatting upscaling
• Gradient-based bicubic spline interpolation for artifact reduction
• 3x-4x faster novel view synthesis than baseline