高斯溅射的即时着色
Instant Colorization of Gaussian Splats
AI总结：
本文提出了一种高效方法，将2D图像信息反向映射到高斯溅射场景中，用于场景复原和语义分割，实现速度提升。
摘要：
高斯溅射最近成为光实3D场景重建和渲染最受欢迎的框架之一。尽管当前光栅化器允许高效地将3D高斯溅射映射到2D相机视图，但本文的工作关注于将2D图像信息（如颜色、神经特征或分割掩码）高效地反向映射到现有的高斯溅射场景上。这种'相反'的方向使应用范围从场景复原和风格化到3D语义分割，但也引入了挑战，如视点依赖的着色和遮挡处理。我们的方法利用正规方程解决每个高斯的可见性加权最小二乘问题，并可以使用现有的可微光栅化器高效实现。我们在场景复原、特征丰富和3D语义分割任务中展示了方法的有效性，相比基于梯度下降的基线方法，速度提升了多达一个数量级。
英文摘要：
Gaussian Splatting has recently become one of the most popular frameworks for photorealistic 3D scene reconstruction and rendering. While current rasterizers allow for efficient mappings of 3D Gaussian splats onto 2D camera views, this work focuses on mapping 2D image information (e.g. color, neural features or segmentation masks) efficiently back onto an existing scene of Gaussian splats. This 'opposite' direction enables applications ranging from scene relighting and stylization to 3D semantic segmentation, but also introduces challenges, such as view-dependent colorization and occlusion handling. Our approach tackles these challenges using the normal equation to solve a visibility-weighted least squares problem for every Gaussian and can be implemented efficiently with existing differentiable rasterizers. We demonstrate the effectiveness of our approach on scene relighting, feature enrichment and 3D semantic segmentation tasks, achieving up to an order of magnitude speedup compared to gradient descent-based baselines.
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。
