Online novel view synthesis remains challenging, requiring robust scene reconstruction from sequential, often unposed, observations. We present ReCoSplat, an autoregressive feed-forward Gaussian Splatting model supporting posed or unposed inputs, with or without camera intrinsics. While assembling local Gaussians using camera poses scales better than canonical-space prediction, it creates a dilemma during training: using ground-truth poses ensures stability but causes a distribution mismatch when predicted poses are used at inference. To address this, we introduce a Render-and-Compare (ReCo) module. ReCo renders the current reconstruction from the predicted viewpoint and compares it with the incoming observation, providing a stable conditioning signal that compensates for pose errors. To support long sequences, we propose a hybrid KV cache compression strategy combining early-layer truncation with chunk-level selective retention, reducing the KV cache size by over 90% for 100+ frames. ReCoSplat achieves state-of-the-art performance across different input settings on both in- and out-of-distribution benchmarks. Code and pretrained models will be released. Our project page is at https://freemancheng.com/ReCoSplat .

翻译：在线新视角合成仍然具有挑战性，需要从连续的、通常无姿态的观测中实现鲁棒的场景重建。我们提出了ReCoSplat，一种支持有姿态或无姿态输入、带或不带相机内参的自回归前馈高斯泼溅模型。虽然使用相机姿态组装局部高斯泼溅比在规范空间进行预测具有更好的可扩展性，但它在训练时产生了一个困境：使用真实姿态能确保稳定性，但在推理时使用预测姿态会导致分布不匹配。为了解决这个问题，我们引入了一个渲染-比较模块。该模块从预测的视角渲染当前重建结果，并将其与输入的观测进行比较，从而提供一个稳定的条件信号以补偿姿态误差。为了支持长序列，我们提出了一种混合键值缓存压缩策略，结合了早期层截断与块级选择性保留，对于100帧以上的序列，可将键值缓存大小减少90%以上。ReCoSplat在不同输入设置下，于分布内和分布外基准测试中均达到了最先进的性能。代码与预训练模型将予以发布。我们的项目页面位于 https://freemancheng.com/ReCoSplat 。