FeCoSplat：用于前馈3D高斯泼溅的反馈引导压缩
FeCoSplat: Feedback-Guided Compression for Feed-Forward 3D Gaussian Splatting
AI总结：
FeCoSplat提出反馈引导压缩框架，通过压缩中间特征并利用渲染视图反馈优化，实现前馈3DGS高效压缩，在低比特率下取得优异率失真性能，接收端仅需3.45M参数。
AI中文摘要：
前馈3D高斯泼溅（3DGS）能够从稀疏多视角图像中实现高效的新视角合成，但其表示在存储和传输方面仍然成本高昂。现有方法要么压缩输入图像，导致接收端重建负担沉重，要么压缩重建的高斯图元，而由于这些图元具有异质和不规则的属性，难以压缩。我们转而压缩紧凑的中间特征，从而在压缩效率与接收端复杂度之间取得更好的平衡。基于这一范式，我们提出了FeCoSplat，一种用于前馈3DGS的反馈引导压缩框架。FeCoSplat首先压缩多视角特征以获得中间3DGS，其渲染视图作为反馈引导第二阶段压缩以进一步细化。生成的比特流被解码为紧凑的隐式状态，并从中通过轻量级预测器重建最终的高斯图元。实验表明，FeCoSplat实现了良好的率失真性能，尤其是在低比特率下，且接收端高斯重建仅需3.45M参数。代码即将发布。
英文摘要：
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.
