EchoGS: Compact 3D Gaussian Splatting via Conditional Response Credit

Compact 3D Gaussian Splatting (3DGS) methods commonly score each Gaussian independently or merge primitives according to parameter similarity. Neither strategy answers the basic compression question: which rendered effects remain irreplaceable after nearby substitutes are considered? We introduce EchoGS, a three-stage compression pipeline centered on conditional response credit. Stage I collects demand to identify where reconstruction capacity is needed. The core Stage II encodes each Gaussian's multi-view rendering behavior in a compact, view-tagged signature and jointly compares signatures within spatial neighborhoods. Local ridge leverage measures each primitive's contribution to response coverage given its neighbors: repeated directions share credit, while sufficiently supported complementary directions retain value. Blending it with marginal demand produces the allocation score passed to Stage III, which distributes the target budget and fits replacement Gaussians by matching assigned geometric moments. In the reported comparison with vanilla 3DGS, the default configuration achieves 9.01×–11.58× count compression on three datasets with competitive rendering quality.