Abstract
Image representation is a fundamental task in computer vision. Recently, Gaussian Splatting has emerged as an efficient representation framework, and its extension to 2D image representation enables lightweight, yet expressive modeling of visual content. While recent 2D Gaussian Splatting (2DGS) approaches provide compact storage and real-time decoding, they often produce blurry or indistinct boundaries when the number of Gaussians is small due to the lack of contour awareness. In this work, we propose a Contour Information-Aware 2D Gaussian Splatting framework that incorporates object segmentation priors into Gaussian-based image representation. By constraining each Gaussian to a specific segmentation region during rasterization, our method prevents cross-boundary blending and preserves edge structures under high compression. We also introduce a warm-up scheme to stabilize training and improve convergence. Experiments on synthetic color charts and the DAVIS dataset demonstrate that our approach achieves higher reconstruction quality around object edges compared to existing 2DGS methods. The improvement is particularly evident in scenarios with very few Gaussians, while our method still maintains fast rendering and low memory usage.
Abstract (translated)
图像表示是计算机视觉中的一个基础任务。最近，高斯点绘（Gaussian Splatting）作为一种高效的表示框架崭露头角，并且其扩展到2D图像表示中使轻量级、但表达能力强的视觉内容建模成为可能。尽管最近提出的二维高斯点绘（2DGS）方法提供了紧凑的存储和实时解码，但在高斯数量较少时，由于缺乏轮廓感知，它们常常会产生模糊或边界不清晰的效果。 在本文工作中，我们提出了一种基于轮廓信息的二维高斯点绘框架，该框架将物体分割先验知识融入到基于高斯分布的图像表示中。通过限制每个高斯分布在栅格化过程中的特定分割区域内，我们的方法可以防止跨边界的混合，并在高度压缩的情况下保留边缘结构。我们还引入了一种预热方案来稳定训练并提高收敛速度。 实验结果表明，在合成颜色图表和DAVIS数据集上，相较于现有的2DGS方法，我们的方法在物体边缘的重建质量方面表现更优。尤其是在高斯数量非常少的情景下，改进尤为显著，而同时我们的方法仍保持快速渲染和低内存使用的特点。
URL
https://arxiv.org/abs/2512.23255
