让3D高斯点云随视角动态调整，重建更精准且泛化更强。
UniqueSplat: View-conditioned 3D Gaussian Splatting for Generalizable 3D Reconstruction
- 用双分支超网络学习视角无关与特有信息，实现动态适配。
- 在RealEstate10K、DTU等数据集上优于现有方法，跨数据集表现突出。
- 适合需要高精度、多视角定制化3D重建的场景应用。
本文提出UniqueSplat，一种视图条件化的前馈3D高斯点云渲染模型，用于为每个视图查询定制化重建3D辐射场。现有前馈方法如pixelSplat和MVSplat通过最小化渲染视图与真实图像间的误差，在所有视图中生成固定高斯分布，但这些固定高斯难以适应特定视角，因预测时未融入目标视图信息。为解决此问题，UniqueSplat将视图条件信息作为先验知识嵌入网络参数，使高斯分布能根据不同视图动态调整。具体地，我们设计了一个双分支视图条件超网络，同时学习视图无关嵌入与视图特异性知识，既挖掘多视图共享信息，又可在测试时适配特定视角。在RealEstate10K、ACID和DTU等常用数据集上的大量实验表明，UniqueSplat显著优于当前最优方法；尤其在跨数据集评估中表现优异，展现了出色的泛化能力。
原文摘要 · Abstract (English)
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.
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