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Textured-GS: Gaussian Splatting with Spatially Defined Color and Opacity

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arxiv 2407.09733 v3 pith:IERKVEBU submitted 2024-07-13 cs.CV

classification cs.CV
keywords textured-gsgaussianrenderingacrossapproachcolordefinedgaussians
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce Textured-GS, an innovative method for rendering Gaussian splatting that incorporates spatially defined color and opacity variations using Spherical Harmonics (SH). This approach enables each Gaussian to exhibit a richer representation by accommodating varying colors and opacities across its surface, significantly enhancing rendering quality compared to traditional methods. To demonstrate the merits of our approach, we have adapted the Mini-Splatting architecture to integrate textured Gaussians without increasing the number of Gaussians. Our experiments across multiple real-world datasets show that Textured-GS consistently outperforms both the baseline Mini-Splatting and standard 3DGS in terms of visual fidelity. The results highlight the potential of Textured-GS to advance Gaussian-based rendering technologies, promising more efficient and high-quality scene reconstructions. Our implementation is available at https://github.com/ZhentaoHuang/Textured-GS.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    Neural Harmonic Textures add periodic feature interpolation and deferred neural decoding to primitive representations, achieving state-of-the-art real-time novel-view synthesis and bridging primitive and neural-field methods.

  2. A$^2$TG: Adaptive Anisotropic Textured Gaussians for Efficient 3D Scene Representation

    cs.CV 2026-01 conditional novelty 6.0 of 10

    Adaptive anisotropic texture allocation reduces memory in textured Gaussian splatting while keeping rendering quality competitive.

  3. R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision

    cs.CV 2025-06 conditional novelty 6.0 of 10

    The survey formalizes degradation-aware rendering for 3D Low-Level Vision and organizes roughly 100 methods on super-resolution, deblurring, weather removal, restoration, and enhancement in NeRF and 3DGS pipelines.

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