FreeSplatter：面向稀疏视角三维重建的无位姿高斯泼溅方法
FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction
AI总结：
针对稀疏视角三维重建需精确相机位姿且获取困难的问题，提出无位姿前馈框架FreeSplatter，通过精简Transformer架构生成三维高斯并同步估计位姿，性能优于位姿依赖型LRMs，位姿精度比肩MASt3R，还可简化3D内容创建流程。
摘要：
稀疏视角重建模型通常需要精确的相机位姿，但从稀疏视角图像中获取这些参数仍然颇具挑战。我们提出FreeSplatter——一种可扩展的前馈框架，可从未标定的稀疏视角图像生成高质量三维高斯（3D Gaussians），同时在数秒内估计相机参数。该方法采用精简的Transformer架构，其中自注意力模块促进多视角图像token间的信息交互，在统一参考框架下将其解码为像素对齐的三维高斯基元。这种表示既能实现高保真三维建模，又能借助现成求解器高效估计相机参数。我们开发了两种专用变体，分别面向以物体为中心的重建和场景级重建，均在综合数据集上完成训练。值得注意的是，FreeSplatter的性能显著优于多种依赖位姿的大型重建模型（LRMs），同时在挑战性基准测试中，其位姿估计精度与当前最先进的无位姿重建方法MASt3R相当甚至更优。除技术基准表现外，FreeSplatter还简化了文本/图像转三维的内容创建流程，消除了相机位姿管理的复杂性，同时实现了卓越的视觉保真度。
英文摘要：
Sparse-view reconstruction models typically require precise camera poses, yet obtaining these parameters from sparse-view images remains challenging. We introduce FreeSplatter, a scalable feed-forward framework that generates high-quality 3D Gaussians from uncalibrated sparse-view images while estimating camera parameters within seconds. Our approach employs a streamlined transformer architecture where self-attention blocks facilitate information exchange among multi-view image tokens, decoding them into pixel-aligned 3D Gaussian primitives within a unified reference frame. This representation enables both high-fidelity 3D modeling and efficient camera parameter estimation using off-the-shelf solvers. We develop two specialized variants--for object-centric and scene-level reconstruction--trained on comprehensive datasets. Remarkably, FreeSplatter outperforms several pose-dependent Large Reconstruction Models (LRMs) by a notable margin while achieving comparable or even better pose estimation accuracy compared to state-of-the-art pose-free reconstruction approach MASt3R in challenging benchmarks. Beyond technical benchmarks, FreeSplatter streamlines text/image-to-3D content creation pipelines, eliminating the complexity of camera pose management while delivering exceptional visual fidelity.
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。
