提出无需迭代优化的图像重建框架，实现毫秒级快速重构。
AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting
- 用分阶段残差结构逐步添加高斯点，自动定位需增强区域。
- 在Kodak和DIV2K上重建质量优于基线，编码时间仅160-300毫秒。
- 适合追求高速、低延迟图像重建的实时应用开发者。
2D高斯点阵提供高效的显式图像表示，但现有方法仍需耗时的逐图像迭代优化或依赖手工先验分配基元。本文提出AIR，一种自监督前馈框架，将迭代高斯拟合过程压缩为单次网络前传，消除测试时逐图像优化。AIR采用分阶段残差架构，从重建残差中逐步预测新增高斯基元，并引入显式阶段控制机制，仅在重建不足区域激活新基元。通过“预测-优化-蒸馏”训练策略，将短视优化增量回蒸馏至预测器以稳定多阶段预测。经各阶段联合微调并配置图像自适应量化器后，实现紧凑的高斯存储。在Kodak与DIV2K数据集上的实验表明，AIR在重建质量上优于代表性高斯基线方法，同时编码时间降至160–300毫秒。
原文摘要 · Abstract (English)
2D Gaussian splatting provides an efficient explicit representation for image reconstruction, but existing methods still require costly per-image iterative optimization or rely on handcrafted priors for primitive allocation. We present AIR, a self-supervised feed-forward framework that amortizes iterative Gaussian fitting into a single network pass, eliminating per-image test-time optimization. AIR adopts a stage-wise residual architecture that progressively predicts additional Gaussian primitives from reconstruction residuals, together with an explicit Stage Control mechanism that activates new primitives only in under-reconstructed regions. A Predict--Optimize--Distill training strategy stabilizes multi-stage prediction by distilling short-horizon optimized Gaussian increments back into the predictor. The stabilized predictor is then jointly finetuned across stages and equipped with an image-adaptive quantizer for compact Gaussian storage. Experiments on Kodak and DIV2K show that AIR achieves better reconstruction quality than representative Gaussian-based baselines while reducing encoding time to 160--300\,ms. Code: https://github.com/whoiszzj/AIR.git
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