用2D高斯点快速生成自适应图像表示，训练速度提升十倍。
Instant GaussianImage: A Generalizable and Self-Adaptive Image Representation via 2D Gaussian Splatting
- 先用网络快速生成粗略高斯表示，再少量微调。
- 训练时间缩短一个数量级，相同高斯数下画质更优。
- 根据图像复杂度动态调整高斯点数量，灵活高效。
隐式神经表示（INR）在图像表示领域取得显著进展，但需要大量GPU资源。GaussianImage首次引入高斯点渲染以降低计算成本，但训练缓慢且每张图的高斯点数量固定，难以适应不同信息熵。为此，本文提出一种通用且自适应的2D高斯点图像表示框架。方法先通过网络快速生成粗略高斯表示，再进行极小量微调，实现与GaussianImage相当的渲染质量，同时大幅减少训练时间。此外，该方法根据图像复杂度动态调整高斯点数量，进一步提升实用性。在DIV2K和Kodak数据集上的实验表明，本方法在极少迭代次数下达到或超过GaussianImage的渲染性能，训练时间最多减少一个数量级，且相同高斯点数下表现更优。
原文摘要 · Abstract (English)
Implicit Neural Representation (INR) has demonstrated remarkable advances in the field of image representation but demands substantial GPU resources. GaussianImage recently pioneered the use of Gaussian Splatting to mitigate this cost, however, the slow training process limits its practicality, and the fixed number of Gaussians per image limits its adaptability to varying information entropy. To address these issues, we propose in this paper a generalizable and self-adaptive image representation framework based on 2D Gaussian Splatting. Our method employs a network to quickly generate a coarse Gaussian representation, followed by minimal fine-tuning steps, achieving comparable rendering quality of GaussianImage while significantly reducing training time. Moreover, our approach dynamically adjusts the number of Gaussian points based on image complexity to further enhance flexibility and efficiency in practice. Experiments on DIV2K and Kodak datasets show that our method matches or exceeds GaussianImage's rendering performance with far fewer iterations and shorter training times. Specifically, our method reduces the training time by up to one order of magnitude while achieving superior rendering performance with the same number of Gaussians.
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