ClearGS：基于手持视频的可靠性感知高斯泼溅

中文摘要 AI 辅助

我们提出ClearGS，用于从手持视频中进行三维高斯泼溅（3DGS），这些视频存在视点覆盖不均和帧质量混合的问题。ClearGS不是用二元决策来选择帧，而是采用可靠性感知视图分配（RVA），根据外观可靠性、退化风险和几何效用分配分级原始监督权重，同时弱激活有用的被抑制帧以维持轨迹覆盖。由于加权无法恢复因模糊或失真而丢失的细节，ClearGS进一步引入渲染引导的视频内恢复（RIVR）。当前的3DGS渲染提供一个姿态对齐的结构候选，一个冻结的无参考恢复专家在没有任何干净参考图像的情况下恢复对应的原始视频观测，无参考感知分数在渲染、恢复观测和高频融合候选之间进行选择。ClearGS随后应用全轨迹修复整合来重新审视已接受的修复并保留早期引入的细节。在GS2E和GSOTM上，ClearGS实现了最先进的整体性能，在大多数退化设置中，CLIP-IQA和MUSIQ持续提升，LPIPS降低，无需成对锐利监督或匹配的干净参考。

英文摘要

We present ClearGS for 3D Gaussian Splatting (3DGS) from handheld videos with uneven viewpoint coverage and mixed frame quality. Rather than selecting frames with binary decisions, ClearGS uses Reliability-aware View Allocation (RVA) to assign graded raw-supervision weights based on appearance reliability, degradation risk, and geometric utility, while weakly reactivating useful suppressed frames to maintain trajectory coverage. Since weighting cannot restore details lost to blur or distortion, ClearGS further introduces Render-Guided In-Video Restoration (RIVR). The current 3DGS render provides a pose-aligned structural candidate, a frozen no-reference restoration expert restores the corresponding raw video observation without any clean reference image, and no-reference perceptual scores select among the render, restored observation, and high-frequency fused candidate. ClearGS then applies Full-Trajectory Repair Consolidation to revisit accepted repairs and preserve details introduced early. On GS2E and GSOTM, ClearGS achieves state-of-the-art overall performance, with consistent CLIP-IQA and MUSIQ gains and LPIPS reductions in most degradation settings, without paired sharp supervision or matched clean references.

发表机构

• Shenzhen University of Advanced Technology（深圳理工大学）
• HONOR（荣耀）
• University of New South Wales（新南威尔士大学）
• Southern University of Science and Technology（南方科技大学）

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