C${}^3$Splat: Exploiting Cross-View Redundancy for Compact Feed-Forward 3D Gaussian Splatting

Recent feed-forward 3D Gaussian Splatting (3DGS) compression methods achieve substantial rate-distortion gains over directly compressing large sets of unordered Gaussian primitives by coding multi-view intermediate features. However, these view-wise latent streams are still entropy-coded independently, overlooking cross-view redundancy and its strong dependence on inter-view geometry. We introduce C${}^3$Splat, a Cross-view Causal Compression framework for Gaussian Splatting, which formulates feature compression in feed-forward 3DGS as cross-view causal entropy modeling. Each view is encoded using only previously decoded views and decoder-available geometry. C${}^3$Splat aligns decoded reference features to the current view using predicted depth and camera geometry, adaptively aggregates references according to spatial reliability, and combines the resulting cross-view condition with the existing single-view hierarchical prior to refine the probability distribution used for arithmetic coding. On the 8-view DL3DV-Benchmark, C${}^3$Splat achieves a PSNR BD-rate of $-21.82\%$ over the state-of-the-art method. We further find that bitrate reduction is strongly correlated with geometric view overlap, confirming that cross-view redundancy is closely tied to shared visible scene content and remains exploitable even after multi-view interaction in the reconstruction backbone. These results establish cross-view causal entropy modeling as an effective mechanism for exploiting cross-view redundancy toward more compact feed-forward 3DGS representations.