ReCoSplat: 基于渲染与比较的自回归前馈高斯点云生成
ReCoSplat: Online Feed-Forward Gaussian Splatting via Render-and-Compare
AI总结：
ReCoSplat通过引入渲染与比较模块，解决高斯点云生成中姿态误差问题，实现高效稳定的视角合成。
摘要：
在线生成新视角仍具有挑战性，需要从连续的、通常未定位的观测中进行稳健的场景重建。我们提出了ReCoSplat，一种支持定位或未定位输入、有或无相机内参的自回归前馈高斯点云生成模型。虽然使用相机姿态组装局部高斯分布比传统空间预测更有效，但在训练过程中却产生了一个矛盾：使用真实姿态确保稳定性，但当在推理时使用预测姿态时会导致分布不匹配。为了解决这个问题，我们引入了渲染与比较（ReCo）模块。ReCo从预测视角渲染当前重建，并将其与输入观测进行比较，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KV缓存压缩策略，结合早期层截断与片段级选择性保留，将KV缓存大小减少超过90%（对于100+帧）。ReCoSplat在不同输入设置下的in-和out-of-distribution基准测试中均取得了最先进的性能。代码和预训练模型将被发布。我们的项目页面位于https://freemancheng.com/ReCoSplat。
英文摘要：
Online novel view synthesis requires a model to reconstruct a scene causally from a stream of observations while keeping it renderable at every moment. We present ReCoSplat, an online feed-forward Gaussian Splatting model supporting both posed and unposed inputs, with or without camera intrinsics. While assembling local Gaussians with camera poses scales better than canonical-space prediction, stable training requires ground-truth poses, creating a distribution mismatch when predicted poses are used at inference. To address this, we introduce a Render-and-Compare (ReCo) module. ReCo renders the accumulated scene from the viewpoint of the incoming observation, comparing the render with the observation to produce a stable conditioning signal that helps bridge the mismatch. 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 or more frames. ReCoSplat achieves state-of-the-art performance among online methods while processing 256-view streams at an average input throughput of 45.1 FPS, with an end-of-stream throughput of 41.1 FPS on an RTX 6000 Ada GPU. Code and pretrained models are released at https://freemancheng.com/ReCoSplat .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
