SelfSplat：无位姿且无3D先验的可泛化3D高斯泼溅
SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting
AI总结：
SelfSplat提出一种无需位姿和3D先验的可泛化3D高斯泼溅方法，通过自监督深度与位姿估计及匹配感知网络实现高质量重建，并在多个真实数据集上取得领先性能。
摘要：
我们提出SelfSplat，一种新颖的3D高斯泼溅模型，旨在从未标定位姿的多视角图像中执行无位姿且无3D先验的可泛化3D重建。由于缺乏真值数据、已学习的几何信息，以及需要在不进行微调的情况下实现精确的3D重建，这些设置在本质上是不适定的，使得传统方法难以获得高质量结果。我们的模型通过有效地将显式3D表示与自监督深度和位姿估计技术相结合来应对这些挑战，从而在位姿精度和3D重建质量方面实现相互提升。此外，我们引入了一个匹配感知的位姿估计网络和一个深度细化模块，以增强跨视图的几何一致性，确保更精确和更稳定的3D重建。为了展示我们方法的性能，我们在大规模真实世界数据集上对其进行了评估，包括RealEstate10K、ACID和DL3DV。SelfSplat在外观和几何质量方面均取得了优于先前最先进方法的结果，同时展现出强大的跨数据集泛化能力。广泛的消融研究和分析也验证了我们所提出方法的有效性。代码和预训练模型可在https://gynjn.github.io/selfsplat/获取。
英文摘要：
We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images. These settings are inherently ill-posed due to the lack of ground-truth data, learned geometric information, and the need to achieve accurate 3D reconstruction without finetuning, making it difficult for conventional methods to achieve high-quality results. Our model addresses these challenges by effectively integrating explicit 3D representations with self-supervised depth and pose estimation techniques, resulting in reciprocal improvements in both pose accuracy and 3D reconstruction quality. Furthermore, we incorporate a matching-aware pose estimation network and a depth refinement module to enhance geometry consistency across views, ensuring more accurate and stable 3D reconstructions. To present the performance of our method, we evaluated it on large-scale real-world datasets, including RealEstate10K, ACID, and DL3DV. SelfSplat achieves superior results over previous state-of-the-art methods in both appearance and geometry quality, also demonstrates strong cross-dataset generalization capabilities. Extensive ablation studies and analysis also validate the effectiveness of our proposed methods. Code and pretrained models are available at https://gynjn.github.io/selfsplat/
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。
