Mipmap-GS：让高斯体随尺度特定Mipmap形变以实现抗锯齿渲染
Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering
AI总结：
针对3DGS单尺度训练导致缩放渲染画质退化的问题，提出Mipmap-GS即插即用优化方法，通过伪真值与尺度一致性损失注入尺度信息，在NeRF Synthetic数据集上缩放场景PSNR均显著优于3DGS。
摘要：
3D高斯泼溅（3D Gaussian Splatting, 3DGS）凭借其卓越的渲染效率和高保真性，在新视角合成领域受到广泛关注。然而，由于单尺度训练得到的表示不可调整，训练后的高斯体在缩放时会出现严重的画质退化。尽管已有部分方法尝试通过对基元的选择性渲染或滤波等后处理技术解决该问题，但高斯体本身并未包含尺度特定信息。本文提出一种统一优化方法，通过自调整基元属性（如颜色、形状、大小）和分布（如位置），使高斯体能够适配任意尺度。受mipmap技术启发，我们为目标尺度设计了伪真值，并提出尺度一致性引导损失，将尺度信息注入3D高斯体中。该方法是一个即插即用模块，可应用于任意3DGS模型，以解决放大和缩小过程中的锯齿问题。大量实验验证了方法的有效性。值得注意的是，在NeRF Synthetic数据集上，我们的方法在PSNR指标上相比3DGS，放大场景平均提升9.25 dB，缩小场景平均提升10.40 dB。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
