ccSplat for Compact and Compressed Feed-Forward 3D Gaussian Splatting

We present ccSplat, a framework for reducing the Gaussian count and storage cost of pixel-aligned feed-forward 3D Gaussian Splatting. Dense prediction can produce over a million primitives per scene. Reducing their count requires preserving surface coverage, while coding the remaining attributes requires exploiting their redundancy. ccSplat connects these tasks through the pixel addresses of retained Gaussians. Its compaction module learns rendering importance, selects candidates under a coverage-aware count budget, and adjusts their scales with a refinement head. The backbone remains frozen. The codec uses the retained pixel grid to recover sparse locations and predict attributes, then allocates rotation precision according to decoded Gaussian shape. On nine Mip-NeRF 360 scenes with SHARP, ccSplat reduces the mean Gaussian count from 1.18M to 0.30M and mean storage from 66.06 to 2.45 MB, a 27.0× storage reduction. Source-view PSNR changes from 27.63 to 27.26 dB after compaction and compression. Additional experiments evaluate compaction on multi-view AnySplat outputs before voxel fusion.