CasDeblurGS：用于从两张模糊图像重建3D高斯溅射的级联2D到3D多视图一致性方法
CasDeblurGS: Cascaded 2D-to-3D Multi-View Consistency for 3D Gaussian Splatting from Two Blurry Images
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中文总结 AI 辅助
CasDeblurGS是一种级联框架，仅用两张已知内参的运动模糊图像即可重建连贯3D场景，在Deblur-NeRF场景上PSNR较基线提升1.19dB和2.11dB，渲染与几何一致性均改善。
中文摘要 AI 辅助
自由视点3D场景媒体对沉浸式应用愈发重要，但实际采集常面临严重的视图稀疏性和运动模糊问题。尽管神经渲染已在稀疏视图合成领域取得进展，现有感知模糊的方法通常需要大量多视图冗余数据、精确的相机位姿或高昂的逐场景优化成本。本文针对一种严格且实用的场景设置开展研究：仅利用两张已知内参的运动模糊图像重建连贯的3D场景，无需输入视图位姿、辅助清晰图像或逐场景测试时优化。为此，本文提出CasDeblurGS，一种级联框架，可逐步从局部2D对应关系中恢复可靠的跨视图信息，直至全局3D引导。第一阶段通过感知遮挡的对应关系过滤构建局部可靠引导；第二阶段将中间恢复结果聚合为临时无位姿的3D高斯表示，其输入视图重渲染结果为最终恢复提供密集全局引导。该方法生成的视图可实现更连贯的3D表示和更高质量的新视图合成。在真实世界和合成Deblur-NeRF场景上的实验表明，该方法相较于强基线方法取得了一致的性能提升，PSNR分别提高了1.19 dB和2.11 dB。渐进式 ablation 实验、跨视图对应关系可视化及相机重投影分析进一步证明，该方法在渲染质量和多视图几何一致性两方面均有改进。
英文摘要
Free-viewpoint 3D scene media is increasingly important for immersive applications, yet practical capture often suffers from severe view sparsity and motion blur. Although neural rendering has advanced sparse-view synthesis, existing blur-aware methods typically require substantial multi-view redundancy, accurate camera poses, or costly per-scene optimization. We address a stringent yet practical setting: reconstructing a coherent 3D scene from only two motion-blurred images with known intrinsics, without input-view poses, auxiliary sharp images, or per-scene test-time optimization. To this end, we propose CasDeblurGS, a cascaded framework that progressively recovers reliable cross-view information from local 2D correspondences to global 3D guidance. Stage 1 constructs locally reliable guidance through occlusion-aware correspondence filtering, while Stage 2 aggregates the intermediate restorations into a provisional pose-free 3D Gaussian representation whose input-view re-renders provide dense global guidance for final restoration. The resulting views enable a more coherent 3D representation and higher-quality novel-view synthesis. Experiments on real-world and synthetic Deblur-NeRF scenes show consistent gains over strong baselines, improving PSNR by 1.19 dB and 2.11 dB, respectively. Progressive ablations, cross-view correspondence visualization, and camera reprojection analysis further demonstrate improvements in both rendering quality and multi-view geometric consistency.
发表机构
- University of Virginia（弗吉尼亚大学）
- KT R&D Center（KT研发中心）
机构由 AI 辅助整理，请以论文原文为准。
