FillGS：通过视点-时间选择与生成细化填补4D高斯溅射中的观测缺口
FillGS: Filling Observation Gaps in 4D Gaussian Splatting via Viewpoint-Time Selection and Generative Refinement
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
FillGS提出主动选择时空虚拟视点的流程，通过过滤不可靠区域微调4DGS，在多视图视频基准上有效填补观测缺口，减少伪影并提升重建效果。
AI中文摘要：
4D高斯溅射（4DGS）可实现动态场景的照片级真实感渲染，但视点覆盖有限时，部分时空区域观测稀疏，会产生伪影，尤其在运动幅度大的场景中更为明显。现有利用生成模型的方法，在细化渲染视图前依赖启发式虚拟视点选择，无法主动探索这类稀疏观测区域。为解决该问题，本文提出一种主动选择时空虚拟视点以改进4DGS重建的流程：基于4D高斯的渲染敏感度与运动感知观测密度，为生成增强选择虚拟视点，优先选择能缓解观测稀疏性的视图；在细化图像中，过滤与捕获观测冲突或可能含生成伪影的区域，仅用可靠区域微调4DGS。本文在多视图视频基准上采用专为诱导观测缺口设计的新训练/测试划分进行评估，结果显示，该方法在定性与定量评估中均较现有视点选择策略及微调方法取得一致改进，同时减少了伪影。
英文摘要：
4D Gaussian Splatting (4DGS) can render dynamic scenes photorealistically. However, with limited viewpoint coverage, some spatiotemporal regions remain sparsely observed, leading to artifacts, particularly in scenes with large motion. Existing approaches leveraging generative models rely on heuristic virtual-viewpoint selection before refining rendered views. As a result, they cannot actively explore such sparsely observed regions. To address this issue, we propose a pipeline that actively selects spatiotemporal virtual viewpoints to improve 4DGS reconstruction. Our method selects virtual viewpoints for generative enhancement based on the rendering sensitivity and motion-aware observation density of 4D Gaussians, prioritizing views that alleviate observation sparsity. In the refined images, we filter out regions that conflict with captured observations or are likely to contain generative artifacts and then fine-tune 4DGS using only the reliable regions. We evaluate our method on multi-view video benchmarks using new train/test splits designed to induce observation gaps. Results show consistent improvements over prior viewpoint selection strategies and fine-tuning methods in both qualitative and quantitative evaluations, while reducing artifacts.
