Gradient-Weighted Feature Back-Projection: A Fast Alternative to Feature Distillation in 3D Gaussian Splatting
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.
我们提出了一种无需训练的高斯点云特征场渲染方法。该方法通过基于每个高斯点在最终渲染中的影响力进行加权求和，将二维特征反投影到预训练的三维高斯点中。与大多数基于训练的特征场渲染方法相比，这些方法在二维分割上表现出色，但在没有后处理的情况下，三维分割表现较差。我们的方法在二维和三维分割中均能实现高质量的结果。实验结果表明，该方法不仅快速且具有良好的可扩展性，其性能可与基于训练的方法相媲美。