StructGS: Adaptive Spherical Harmonics and Rendering Enhancements for Superior 3D Gaussian Splatting

2025-03-09

AI Summary

To address key limitations of 3D Gaussian Splatting (3DGS)—including inadequate modeling of non-local structures, inefficient spherical harmonic (SH) initialization, and excessive memory consumption during high-resolution rendering—this paper proposes a structure-enhanced Gaussian splatting framework. Our method introduces three core innovations: (1) a dynamic SH initialization mechanism that adaptively activates higher-order terms to reduce early computational redundancy; (2) a patch-based SSIM-aware loss to improve structural consistency and fine-detail fidelity; and (3) a lightweight multi-scale residual network (MSRN) enabling high-fidelity high-resolution rendering from low-resolution inputs. Evaluated on multiple standard benchmarks, our approach achieves state-of-the-art performance—delivering superior rendering quality with fewer artifacts—while simultaneously reducing GPU memory usage and training time.

Technology Category

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

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
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Recent advancements in 3D reconstruction coupled with neural rendering techniques have greatly improved the creation of photo-realistic 3D scenes, influencing both academic research and industry applications. The technique of 3D Gaussian Splatting and its variants incorporate the strengths of both primitive-based and volumetric representations, achieving superior rendering quality. While 3D Geometric Scattering (3DGS) and its variants have advanced the field of 3D representation, they fall short in capturing the stochastic properties of non-local structural information during the training process. Additionally, the initialisation of spherical functions in 3DGS-based methods often fails to engage higher-order terms in early training rounds, leading to unnecessary computational overhead as training progresses. Furthermore, current 3DGS-based approaches require training on higher resolution images to render higher resolution outputs, significantly increasing memory demands and prolonging training durations. We introduce StructGS, a framework that enhances 3D Gaussian Splatting (3DGS) for improved novel-view synthesis in 3D reconstruction. StructGS innovatively incorporates a patch-based SSIM loss, dynamic spherical harmonics initialisation and a Multi-scale Residual Network (MSRN) to address the above-mentioned limitations, respectively. Our framework significantly reduces computational redundancy, enhances detail capture and supports high-resolution rendering from low-resolution inputs. Experimentally, StructGS demonstrates superior performance over state-of-the-art (SOTA) models, achieving higher quality and more detailed renderings with fewer artifacts.

Problem

• Improves 3D Gaussian Splatting for better novel-view synthesis.
• Reduces computational overhead in 3DGS-based methods.
• Enables high-resolution rendering from low-resolution inputs.

Innovation

• Patch-based SSIM loss enhances detail capture.
• Dynamic spherical harmonics initialization reduces computational overhead.
• Multi-scale Residual Network supports high-resolution rendering.