Superpixel-guided Sampling for Compact 3D Gaussian Splatting

In this paper, we propose to integrate superpixel-guided sampling into the framework of 3D Gaussian Splatting for novel view synthesis. Given a sequence of frames, where each frame is a pair of RGB-D image and the camera pose that captures the image, we first select the keyframes. Then, we decompose each keyframe image into superpixels, back-project each superpixel’s center into 3D space, and initialize a 3D Gaussian at the back-projected position. This superpixel-guided sampling produces a set of sparse but well-distributed Gaussians, which enables the optimization procedure to converge quickly. The experimental results show that with a significantly reduced computing cost, we can synthesize a novel view the quality of which is comparable to that generated by the state-of-the-art methods.