Sparse Light Field Sampling Improves Casual 3D and 4D Reconstruction

2026-08-20

AI Summary

研究探讨了利用多视角而非单视角进行3D和4D重建，通过分析传感器限制与曝光限制下的多视角情况，引入新数据集评估稀疏视图方法，证明即使在低基线条件下使用多个摄像头也能显著提高重建质量。

Abstract

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.

Problem

• sparse light field sampling
• 3D reconstruction
• 4D reconstruction
• multi-view cameras
• reconstruction quality

Innovation

• Sparse Light Field Sampling
• Multi-view Reconstruction
• Angular Diversity