[
    {
        "id": "osp-24724",
        "type": "article-journal",
        "title": "FeCoSplat: Feedback-Guided Compression for Feed-Forward 3D Gaussian Splatting",
        "author": [
            {
                "family": "Li",
                "given": "Yuxuan"
            },
            {
                "family": "Chen",
                "given": "Yihang"
            },
            {
                "family": "Zhang",
                "given": "Yufeng"
            },
            {
                "family": "Cai",
                "given": "Jianfei"
            },
            {
                "family": "Lin",
                "given": "Weiyao"
            }
        ],
        "URL": "https://omanscience.com/en/articles/fecosplat-feedback-guided-compression-for-feed-forward-3d-gaussian-splatting",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Feed-forward 3D Gaussian Splatting (3DGS) enables efficient novel-view synthesis from sparse multi-view images, yet its representations remain costly to store and transmit. Existing approaches compress either the input images, incurring heavy receiver-side reconstruction, or the reconstructed Gaussian primitives, which are difficult to compress due to their heterogeneous and irregular attributes. We instead compress compact intermediate features, providing a better balance between compression efficiency and receiver-side complexity. Based on this paradigm, we propose FeCoSplat, a feedback-guided compression framework for feed-forward 3DGS. FeCoSplat first compresses multi-view features to obtain an intermediate 3DGS, whose rendered views are used as feedback to guide a second-stage compression for further refinement. The resulting bitstreams are decoded into a compact implicit state, from which the final Gaussian primitives are reconstructed with a lightweight predictor. Experiments demonstrate that FeCoSplat achieves favorable rate--distortion performance, particularly at low bitrates, while requiring only 3.45M parameters for receiver-side Gaussian reconstruction. Code will be released soon."
    }
]