Gaussian splatting and single/multi-view depth estimation are typically studied in isolation. In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation and study their interactions. More specifically, we first contribute a robust multi-view depth model by leveraging pre-trained monocular depth features, leading to high-quality feed-forward 3D Gaussian splatting reconstructions. We also show that Gaussian splatting can serve as an unsupervised pre-training objective for learning powerful depth models from large-scale unlabelled datasets. We validate the synergy between Gaussian splatting and depth estimation through extensive ablation and cross-task transfer experiments. Our DepthSplat achieves state-of-the-art performance on ScanNet, RealEstate10K and DL3DV datasets in terms of both depth estimation and novel view synthesis, demonstrating the mutual benefits of connecting both tasks.
高斯散射和单视角/多视角深度估计通常是独立研究的。在本文中，我们提出了DepthSplat，旨在连接高斯散射和深度估计，并研究它们之间的相互作用。具体而言，我们首先通过利用预训练的单目深度特征，贡献了一个鲁棒的多视角深度模型，从而实现了高质量的前馈式3D高斯散射重建。我们还展示了高斯散射可以作为一种无监督的预训练目标，从大规模未标注数据集中学习强大的深度模型。通过广泛的消融实验和跨任务转移实验，我们验证了高斯散射与深度估计之间的协同作用。我们的DepthSplat在ScanNet、RealEstate10K和DL3DV数据集上，在深度估计和新视角合成方面均达到了最先进的性能，展示了连接这两项任务的互惠优势。