AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting

• URL: http://arxiv.org/abs/2605.20820v1
• Date: Wed, 20 May 2026 07:17:22 GMT
• Title: AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting
• Abstract summary: 2D Gaussian splatting provides an efficient explicit representation for image reconstruction.<n>Existing methods still require costly per-image iterative optimization or rely on handcrafted priors for primitive allocation.<n>We present AIR, a self-supervised feed-forward framework that amortizes iterative Gaussian fitting into a single network pass.
• Score: 21.638608939423673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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