MVPGS: Excavating Multi-view Priors for Gaussian Splatting from Sparse Input Views

• URL: http://arxiv.org/abs/2409.14316v1
• Date: Sun, 22 Sep 2024 05:07:20 GMT
• Title: MVPGS: Excavating Multi-view Priors for Gaussian Splatting from Sparse Input Views
• Authors: Wangze Xu, Huachen Gao, Shihe Shen, Rui Peng, Jianbo Jiao, Ronggang Wang,
• Abstract summary: Novel View Synthesis (NVS) is a significant challenge in 3D vision applications. We propose textbfMVPGS, a few-shot NVS method that excavates the multi-view priors based on 3D Gaussian Splatting. Experiments show that the proposed method achieves state-of-the-art performance with real-time rendering speed.
• Score: 27.47491233656671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the Neural Radiance Field (NeRF) advancement has facilitated few-shot Novel View Synthesis (NVS), which is a significant challenge in 3D vision applications. Despite numerous attempts to reduce the dense input requirement in NeRF, it still suffers from time-consumed training and rendering processes. More recently, 3D Gaussian Splatting (3DGS) achieves real-time high-quality rendering with an explicit point-based representation. However, similar to NeRF, it tends to overfit the train views for lack of constraints. In this paper, we propose \textbf{MVPGS}, a few-shot NVS method that excavates the multi-view priors based on 3D Gaussian Splatting. We leverage the recent learning-based Multi-view Stereo (MVS) to enhance the quality of geometric initialization for 3DGS. To mitigate overfitting, we propose a forward-warping method for additional appearance constraints conforming to scenes based on the computed geometry. Furthermore, we introduce a view-consistent geometry constraint for Gaussian parameters to facilitate proper optimization convergence and utilize a monocular depth regularization as compensation. Experiments show that the proposed method achieves state-of-the-art performance with real-time rendering speed. Project page: https://zezeaaa.github.io/projects/MVPGS/

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