TranSplat：基于Transformer从稀疏多视图图像的可泛化3D高斯泼溅
TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Transformers
AI总结：
针对稀疏多视图下可泛化3D高斯泼溅因非重叠和相似区域导致特征匹配难、重建精度低的问题，提出TranSplat，利用深度置信图指导局部匹配并引入单目深度先验，在多个基准上取得最佳性能且泛化能力强。
摘要：
与NeRF等以往的3D重建方法相比，近期可泛化3D高斯泼溅（G-3DGS）方法即使在稀疏视图设置下也展现出令人印象深刻的效率。然而，现有G-3DGS方法出色的重建性能严重依赖于准确的多视图特征匹配，这极具挑战性。特别是对于各视图间存在大量非重叠区域且包含众多相似区域的场景，现有方法的匹配性能较差，重建精度受限。为解决此问题，我们开发了一种利用预测深度置信图来指导准确局部特征匹配的策略。此外，我们提出利用现有单目深度估计模型的知识作为先验，以提升视图间非重叠区域的深度估计精度。结合所提策略，我们提出了一种名为TranSplat的新型G-3DGS方法，在RealEstate10K和ACID基准上均取得最佳性能，同时保持极具竞争力的速度并展现出强大的跨数据集泛化能力。
英文摘要：
Compared with previous 3D reconstruction methods like Nerf, recent Generalizable 3D Gaussian Splatting (G-3DGS) methods demonstrate impressive efficiency even in the sparse-view setting. However, the promising reconstruction performance of existing G-3DGS methods relies heavily on accurate multi-view feature matching, which is quite challenging. Especially for the scenes that have many non-overlapping areas between various views and contain numerous similar regions, the matching performance of existing methods is poor and the reconstruction precision is limited. To address this problem, we develop a strategy that utilizes a predicted depth confidence map to guide accurate local feature matching. In addition, we propose to utilize the knowledge of existing monocular depth estimation models as prior to boost the depth estimation precision in non-overlapping areas between views. Combining the proposed strategies, we present a novel G-3DGS method named TranSplat, which obtains the best performance on both the RealEstate10K and ACID benchmarks while maintaining competitive speed and presenting strong cross-dataset generalization ability. Our code, and demos will be available at: https://xingyoujun.github.io/transplat.
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。
