无需训练即可实现高质量2D与3D分割的特征回投影方法
Gradient-Weighted Feature Back-Projection: A Fast Alternative to Feature Distillation in 3D Gaussian Splatting
- 用梯度加权将2D特征回投影到预训练3D高斯中
- 在2D和3D分割上均达到接近训练方法的性能
- 速度快、可扩展，适合实时渲染场景
我们提出一种无需训练的高斯点阵特征场渲染方法。该方法将2D特征通过加权求和方式回投影至预训练的3D高斯中，权重基于每个高斯在最终渲染中的影响。相较于多数依赖训练的特征场渲染方法在2D分割表现优异但3D分割需后处理的问题，本方法在2D与3D分割上均取得高质量结果。实验表明，该方法速度快、可扩展，性能可媲美训练型方法。
原文摘要 · Abstract (English)
We introduce a training-free method for feature field rendering in Gaussian splatting. Our approach back-projects 2D features into pre-trained 3D Gaussians, using a weighted sum based on each Gaussian's influence in the final rendering. While most training-based feature field rendering methods excel at 2D segmentation but perform poorly at 3D segmentation without post-processing, our method achieves high-quality results in both 2D and 3D segmentation. Experimental results demonstrate that our approach is fast, scalable, and offers performance comparable to training-based methods.
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