多视角采样比单视角重建效果更好，尤其在拍摄条件受限时。
Sparse Light Field Sampling Improves Casual 3D and 4D Reconstruction
- 用多个摄像头采集稀疏视角，提升3D/4D重建质量。
- 少曝光情况下，角度采样比空间分辨率更关键，提升明显。
- 适合手机、轻量级相机等设备的快速动态场景重建。
许多消费级智能手机、双目相机和光场相机可在单次曝光中记录多个同步视角。然而，现有新视角合成流程通常仅使用单视角流，并依赖相机运动或学习先验来获得角度覆盖。本文提出：为何只用一个视角？我们分析了传感器受限多视角（一个传感器需在空间与角度分辨率间权衡）和曝光受限多视角（多个传感器在同一设备上同时捕捉同一事件）。我们引入一个包含三种类型消费级多视角相机的新数据集，评估稀疏视角3DGS与4DGS基线模型，测量重建质量随曝光次数和极端视角夹角的变化。结果表明，即使基线较低，使用多个摄像头也能显著提升单次拍摄、少量拍摄及随意视频场景下的重建质量。此外，在固定传感器预算下，当曝光有限时，角度采样优于空间分辨率，尤其在单次拍摄和动态场景中，静态单视角相机缺乏角度多样性，难以恢复场景几何与运动。
原文摘要 · Abstract (English)
Many consumer smartphones, stereo cameras, and light field cameras record multiple synchronized viewpoints in a single exposure event. However, novel view synthesis pipelines commonly use only a monocular stream and rely on camera motion or learned priors to obtain angular coverage. In this paper, we ask: why do we use only one viewpoint? We analyze sensor-limited multi-view, where one sensor trades off spatial and angular resolution, and exposure-limited multi-view, where multiple sensors on one commodity device observe each event simultaneously. We introduce a new dataset incorporating three types of commodity multi-view cameras, and evaluate sparse-view 3DGS and 4DGS baselines measuring reconstruction quality as a function of number of exposures and angle between extreme views. Our results demonstrate that using multiple cameras, even with a low baseline, significantly improves reconstruction quality in single-shot, few-shot, and casual video settings. In addition, under a fixed sensor budget, angular sampling improves reconstruction when exposures are scarce despite lower spatial resolution. The gains are most pronounced for single-shot and dynamic scenes, where a stationary monocular camera lacks the angular diversity to recover scene geometry and motion.
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