This repo contains the cuda implementation of variables for calculating Gaussian flow (both forward and backward). While the original repo of 3D Gaussian Splatting is here.

git clone --recursive https://github.com/Zerg-Overmind/diff-gaussian-rasterization
pip install ./diff-gaussian-rasterization

rendered_image, radii, rendered_depth, rendered_alpha, proj_means_2D, conic_2D, conic_2D_inv, gs_per_pixel, weight_per_gs_pixel, x_mu = rasterizer(
        means3D = means3D_final,
        means2D = means2D,
        shs = shs,
        colors_precomp = colors_precomp,
        opacities = opacity,
        scales = scales_final,
        rotations = rotations_final,
        cov3D_precomp = cov3D_precomp)

Where proj_means_2D are the coordinates of Gaussians in image plane, conic_2D are the inverse of 2D covariance matrices of Gaussians, conic_2D_inv are the 2D covariance matrices of Gaussians, gs_per_pixel are indices of top-K Gaussians per pixel, weight_per_gs_pixel are weights of top-K Gaussians per pixel, and x_mu are x-mu.

Feel free to change K from 20 (as in the paper) to another value by modifying here and here.

Acknowledgments: We thank the following great works DreamGaussian, DreamGaussian4D, Consistent4D, RT-4DGS, Dynamic3DGaussians, and 3DGS for their codes.