SuperGS: Super-Resolution 3D Gaussian Splatting via Latent Feature Field and Gradient-guided Splitting

• URL: http://arxiv.org/abs/2410.02571v2
• Date: Mon, 7 Oct 2024 18:28:53 GMT
• Title: SuperGS: Super-Resolution 3D Gaussian Splatting via Latent Feature Field and Gradient-guided Splitting
• Authors: Shiyun Xie, Zhiru Wang, Yinghao Zhu, Chengwei Pan,
• Abstract summary: SuperResolution 3DGS (SuperGS) is an expansion of 3DGS designed with a two-stage coarse-to-fine training framework. SuperGS surpasses state-of-the-art HRNVS methods on challenging real-world datasets using only low-resolution inputs.
• Score: 3.5757604402398697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, 3D Gaussian Splatting (3DGS) has exceled in novel view synthesis with its real-time rendering capabilities and superior quality. However, it faces challenges for high-resolution novel view synthesis (HRNVS) due to the coarse nature of primitives derived from low-resolution input views. To address this issue, we propose Super-Resolution 3DGS (SuperGS), which is an expansion of 3DGS designed with a two-stage coarse-to-fine training framework, utilizing pretrained low-resolution scene representation as an initialization for super-resolution optimization. Moreover, we introduce Multi-resolution Feature Gaussian Splatting (MFGS) to incorporates a latent feature field for flexible feature sampling and Gradient-guided Selective Splitting (GSS) for effective Gaussian upsampling. By integrating these strategies within the coarse-to-fine framework ensure both high fidelity and memory efficiency. Extensive experiments demonstrate that SuperGS surpasses state-of-the-art HRNVS methods on challenging real-world datasets using only low-resolution inputs.

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