3D Gaussian Splatting (3DGS) has attracted great attention in novel view synthesis because of its superior rendering efficiency and high fidelity. However, the trained Gaussians suffer from severe zooming degradation due to non-adjustable representation derived from single-scale training. Though some methods attempt to tackle this problem via post-processing techniques such as selective rendering or filtering techniques towards primitives, the scale-specific information is not involved in Gaussians. In this paper, we propose a unified optimization method to make Gaussians adaptive for arbitrary scales by self-adjusting the primitive properties (e.g., color, shape and size) and distribution (e.g., position). Inspired by the mipmap technique, we design pseudo ground-truth for the target scale and propose a scale-consistency guidance loss to inject scale information into 3D Gaussians. Our method is a plug-in module, applicable for any 3DGS models to solve the zoom-in and zoom-out aliasing. Extensive experiments demonstrate the effectiveness of our method. Notably, our method outperforms 3DGS in PSNR by an average of 9.25 dB for zoom-in and 10.40 dB for zoom-out on the NeRF Synthetic dataset.
3D高斯点绘（3DGS）由于其出色的渲染效率和高保真度，在新视角合成领域引起了广泛关注。然而，由于训练过程中采用单尺度表示，训练后的高斯模型在缩放时会出现严重的降质问题。虽然一些方法尝试通过后处理技术（如选择性渲染或针对基元的过滤技术）来解决这一问题，但这些方法并未将尺度特定的信息融入到高斯模型中。在本文中，我们提出了一种统一的优化方法，使高斯模型能够自适应任意尺度，通过自我调整基元属性（如颜色、形状和大小）和分布（如位置）来解决这一问题。 受mipmap技术的启发，我们设计了目标尺度的伪真实值，并提出了尺度一致性引导损失，将尺度信息注入到3D高斯模型中。我们的方法是一种插件模块，适用于任何3DGS模型，以解决放大和缩小时的锯齿问题。大量实验验证了我们方法的有效性。值得注意的是，在NeRF Synthetic数据集上，我们的方法在放大和缩小时相较于3DGS，分别在PSNR上平均提高了9.25 dB和10.40 dB。