A Major Update to V1.0.0

• Comparing to the official implementation, gsplat enables up to 4x less training memory footprint, and up to 2x less training time on Mip-NeRF 360 captures, and potential more on larger scenes.

• Support extremely large scene rendering, which is magnitudes faster than the official CUDA backend diff-gaussian-rasterization.

• Extra features, including batch rasterization, N-D feature rendering (faster), depth rendering, sparse gradient etc.