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arXiv 2605.10705cs.CV

TransmissiveGS:基于残差引导的解耦高斯点散射用于透射场景重建与渲染

TransmissiveGS: Residual-Guided Disentangled Gaussian Splatting for Transmissive Scene Reconstruction and Rendering

HKUST(香港理工大学)

Zhenyu Liang, Xiao Zhang, Tianchao Li, Jack C. P. Cheng, Chi-Keung Tang

AI总结:

本文提出TransmissiveGS框架,通过双高斯表示和延迟着色函数解耦透射场景的反射与透射,利用多视图不一致性分离反射和透射,并引入高频正则化和新合成数据集提升重建与渲染质量。

摘要:

透射场景在日常生活中普遍存在,但其重建和渲染极具挑战性,因为周围环境对透射表面的近场反射与场景后方的透射内容之间存在固有耦合,导致每个观测中出现双重表面几何和双重光谱成分,给标准方法带来歧义。我们提出了TransmissiveGS,一种用于解耦透射场景重建与渲染的新框架。具体而言,我们用双高斯表示建模场景,并引入延迟着色函数以联合渲染两个高斯组件。为了分离反射与透射,我们利用反射的固有多视图不一致性,并利用重建多视图一致内容的残差作为解耦几何和外观建模的线索。我们进一步提出一种反射光场,以实现高保真的近场反射估计。在训练过程中,我们引入高频正则化以保留细节。我们还贡献了一个新的合成数据集用于评估透射表面重建。在合成和真实场景上的实验表明,TransmissiveGS在透射场景的重建和渲染质量上始终优于基于高斯点散射的先前方法。

英文摘要:

Transmissive scenes are ubiquitous in daily life, yet reconstructing and rendering them remains highly challenging due to the inherent entanglement between near-field reflections from the surrounding environment on the transmissive surface, and the transmitted content of the scene behind it. This coupling gives rise to dual surface geometries and dual radiance components within each observation, posing ambiguities for standard methods. We present TransmissiveGS, a novel framework for disentangled reconstruction and rendering of transmissive scenes. Specifically, we model the scene with a dual-Gaussian representation and introduce a deferred shading function to jointly render the two Gaussian components. To separate reflection and transmission, we exploit the inherent multi-view inconsistency of reflections and leverage the residuals from reconstructing multi-view consistent content as cues for disentangled geometry and appearance modeling. We further propose a reflection light field that enables high-fidelity estimation of near-field reflections. During training, we introduce a high-frequency regularization to preserve fine details. We also contribute a new synthetic dataset for evaluating transmissive surface reconstruction. Experiments on both synthetic and real-world scenes demonstrate that TransmissiveGS consistently outperforms prior Gaussian Splatting-based methods in both reconstruction and rendering quality for transmissive scenes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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