DepthSplat: Connecting Gaussian Splatting and Depth

Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, Marc Pollefeys

Keywords: Gaussian Splatting, Multi-View Depth, Monocular Depth

TL;DR: Enjoy the mutual benefits by connecting Gaussian splatting and depth

Abstract: 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. We invite the readers to view our supplementary video for feed-forward reconstruction results of large-scale or 360 scenes. Our code and models will be publicly available.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1388