FlashSplat: 2D to 3D Gaussian Splatting Segmentation Solved Optimally
This solves the challenge of slow and sub-optimal 3D segmentation for computer vision applications, though it is incremental as it builds on existing 3D-GS frameworks.
This study tackled the problem of segmenting 3D Gaussian Splatting from 2D masks by proposing a globally optimal solver that uses linear programming, achieving optimization within 30 seconds, which is about 50 times faster than existing methods.
This study addresses the challenge of accurately segmenting 3D Gaussian Splatting from 2D masks. Conventional methods often rely on iterative gradient descent to assign each Gaussian a unique label, leading to lengthy optimization and sub-optimal solutions. Instead, we propose a straightforward yet globally optimal solver for 3D-GS segmentation. The core insight of our method is that, with a reconstructed 3D-GS scene, the rendering of the 2D masks is essentially a linear function with respect to the labels of each Gaussian. As such, the optimal label assignment can be solved via linear programming in closed form. This solution capitalizes on the alpha blending characteristic of the splatting process for single step optimization. By incorporating the background bias in our objective function, our method shows superior robustness in 3D segmentation against noises. Remarkably, our optimization completes within 30 seconds, about 50$\times$ faster than the best existing methods. Extensive experiments demonstrate the efficiency and robustness of our method in segmenting various scenes, and its superior performance in downstream tasks such as object removal and inpainting. Demos and code will be available at https://github.com/florinshen/FlashSplat.