F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uniformly allocate Gaussians, leading to redundant Gaussians across views. Moreover, they lack an effective mechanism to control the total number of Gaussians while maintaining reconstruction fidelity. To address these limitations, we present F4Splat, which performs Feed-Forward predictive densification for Feed-Forward 3D Gaussian Splatting, introducing a densification-score-guided allocation strategy that adaptively distributes Gaussians according to spatial complexity and multi-view overlap. Our model predicts per-region densification scores to estimate the required Gaussian density and allows explicit control over the final Gaussian budget without retraining. This spatially adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, producing compact yet high-quality 3D representations. Extensive experiments demonstrate that our model achieves superior novel-view synthesis performance compared to prior uncalibrated feed-forward methods, while using significantly fewer Gaussians.
69c243afc105f45cb04820ab
Injae Kim
claimed_verified
2026-03-24T09:20:32.717Z
6315f8d029411a6864b045ff
/avatars/80787599cf695f0012063852f0ef224a.svg
Injae Kim
Knowing
user
Knowing
69c243afc105f45cb04820ac
Chaehyeon Kim
69c243afc105f45cb04820ad
Minseong Bae
claimed_verified
2026-03-24T09:20:30.852Z
64549b4cc13cdb83f1100b94
/avatars/4e809bed509e6ce2dd5532730af81a12.svg
Minseong Bae
KyleBae1017
user
KyleBae1017
69c243afc105f45cb04820ae
Minseok Joo
69c243afc105f45cb04820af
Hyunwoo J. Kim
F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uniformly allocate Gaussians, leading to redundant Gaussians across views. Moreover, they lack an effective mechanism to control the total number of Gaussians while maintaining reconstruction fidelity. To address these limitations, we present F4Splat, which performs Feed-Forward predictive densification for Feed-Forward 3D Gaussian Splatting, introducing a densification-score-guided allocation strategy that adaptively distributes Gaussians according to spatial complexity and multi-view overlap. Our model predicts per-region densification scores to estimate the required Gaussian density and allows explicit control over the final Gaussian budget without retraining. This spatially adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, producing compact yet high-quality 3D representations. Extensive experiments demonstrate that our model achieves superior novel-view synthesis performance compared to prior uncalibrated feed-forward methods, while using significantly fewer Gaussians.
F4Splat introduces a predictive densification approach for 3D Gaussian splatting that adaptively allocates Gaussians based on spatial complexity and view overlap, reducing redundancy while maintaining reconstruction quality.
3D Gaussian Splatting feed-forward densification Gaussian allocation multi-view overlap predictive densification spatial complexity novel-view synthesis