Instant GaussianImage：基于2D Gaussian Splatting的泛化与自适应图像表示
Instant GaussianImage: A Generalizable and Self-Adaptive Image Representation via 2D Gaussian Splatting
AI总结：
本文提出一种基于2D Gaussian Splatting的泛化与自适应图像表示框架，通过网络快速生成粗略表示并微调，同时根据图像复杂度动态调整Gaussian点数量，在大幅缩短训练时间的同时达到或超越GaussianImage的渲染性能。
摘要：
隐式神经表示（INR）在图像表示领域展现出显著进展，但需要大量GPU资源。GaussianImage最近开创性地使用Gaussian Splatting来降低这一成本，然而，缓慢的训练过程限制了其实用性，且每张图像固定数量的Gaussian限制了对不同信息熵的适应性。为了解决这些问题，我们在本文中提出了一种基于2D Gaussian Splatting的泛化与自适应图像表示框架。我们的方法采用网络快速生成粗糙的Gaussian表示，随后进行极少量的微调步骤，在大幅减少训练时间的同时，实现了与GaussianImage相当的渲染质量。此外，我们的方法根据图像复杂度动态调整Gaussian点的数量，以进一步提升实际应用中的灵活性与效率。在DIV2K和Kodak数据集上的实验表明，我们的方法以远少于GaussianImage的迭代次数和更短的训练时间，达到或超越了其渲染性能。具体而言，我们的方法将训练时间最多缩短了一个数量级，同时在相同数量的Gaussian下实现了更优的渲染性能。
英文摘要：
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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