发表机构
Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出视角条件化前馈3D高斯溅射模型UniqueSplat，通过双分支视角条件化超网络实现高斯动态调整，在多数据集及跨数据集评估中均优于现有最优方法，提升了通用3D重建性能。
AI 中文摘要
本文提出UniqueSplat，一种视角条件化前馈3D高斯溅射模型，用于为每个视角查询重建定制化3D辐射场。现有像素Splat、MVSplat等前馈方法，旨在通过最小化渲染视角与真实图像的误差，为每个场景的所有视角生成固定高斯。然而这类固定高斯通常会渲染所有视角的图像，且因预测高斯时未纳入目标视角信息，缺乏适配特定视点的能力。为解决该问题，UniqueSplat将视角条件化信息学习为先验并整合到网络参数中，使高斯能根据不同视角动态调整。具体而言，本文提出双分支视角条件化超网络，同时学习视角无关嵌入与视角特定知识，既挖掘各视角的可共享知识，又在测试时使模型适配特定视角。在RealEstate10K、ACID、DTU等广泛使用的数据集上开展的大量实验表明，UniqueSplat优于现有最优方法；此外，UniqueSplat在跨数据集评估中也显著优于现有方法，展现出出色的泛化能力。
英文摘要
In this paper, we propose UniqueSplat, a view-conditioned feed-forward 3D Gaussian Splatting model to reconstruct customized 3D radiance fields for each view query. Existing feed-forward methods such as pixelSplat and MVSplat aim to generate fixed Gaussians across all views of each scene by minimizing the error between rendered views and ground-truth images. However, such fixed Gaussians generally render images from all views and lack the ability to adapt to specific viewpoints, as they do not incorporate target view information when predicting Gaussians. To address this, our UniqueSplat learns the view-conditioned information as a prior and incorporates this knowledge into network parameters, so that Gaussians are dynamically adjusted in accordance with different views. Specifically, we propose a two-branch view-conditioned hyperNetwork to simultaneously learn view-agnostic embeddings and view-specific knowledge, which not only explores the shareable knowledge from various views, but also adapts the model to specific views at test time. Extensive experiments on widely-used datasets including RealEstate10K, ACID and DTU demonstrate the superiority of UniqueSplat over the state-of-the-art methods. Moreover, UniqueSplat encouragingly outperforms existing methods in cross-dataset evaluation, showing its notable generalization ability.
