发表机构
Nankai University（南开大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AESplat提出解耦外观建模策略，直接推导零阶SH系数并预测高阶系数，在无姿态前馈3DGS中显著提升渲染质量，PSNR较NAS3R提升0.8dB，较DepthSplat提升1.1dB。
AI 中文摘要
无姿态前馈3D高斯泼溅（3DGS）在广义新视角合成方面展现出了巨大的潜力。然而，现有方法通常以相同方式预测由球谐函数（SH）表示的高斯外观属性，忽略了视角无关与视角相关外观之间的根本区别，导致渲染质量欠佳。在本文中，我们提出了AESplat，一个新颖且通用的无姿态前馈3DGS框架，它基于对SH的分析引入了一种有效的解耦外观建模策略，从而实现更高质量的渲染。具体来说，AESplat直接从输入图像中无训练地推导出零阶SH系数，该系数代表基础的视角无关外观成分。随后，高阶SH系数由一个配备两种高效3D感知归纳偏置的浅层多层感知机预测，以建模视角相关的外观变化。在多个数据集上的大量实验表明，我们的方法显著优于最先进的方法，在RealEstate10K数据集上，相比无姿态方法NAS3R，PSNR提高了0.8 dB；相比需要姿态的方法DepthSplat，PSNR提高了1.1 dB。项目页面：此https URL。
英文摘要
Pose-free feed-forward 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential for generalized novel view synthesis. However, existing methods typically predict Gaussian appearance attributes represented by spherical harmonics (SH) in the same manner, overlooking the fundamental distinction between view-independent and view-dependent appearance, which results in suboptimal rendering quality. In this paper, we present AESplat, a novel and general framework for pose-free feed-forward 3DGS that introduces an effective decoupled appearance modeling strategy based on an analysis of SH, enabling higher-quality rendering. Specifically, AESplat directly derives the zeroth-order SH coefficient, which represents the base view-independent appearance component, from the input images without training. The higher-order SH coefficients are subsequently predicted by a shallow multilayer perceptron equipped with two efficient 3D-aware inductive biases to model view-dependent appearance variations. Extensive experiments across multiple datasets demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a $0.8$ dB improvement in PSNR over the pose-free method NAS3R and a $1.1$ dB improvement over the pose-required method DepthSplat on the RealEstate10K dataset. Project page: https://aesplat.github.io/.
