Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints. This paper introduces a general approach to obtain a 4D representation of complex dynamic scenes from multi-view wide-baseline static or moving cameras without prior knowledge of the scene structure, appearance, or illumination. Contributions of the work are: an automatic method for initial coarse reconstruction to initialize joint estimation; sparse-to-dense temporal correspondence integrated with joint multi-view segmentation and reconstruction to introduce temporal coherence; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes by introducing shape constraint. Comparison with state-of-the-art approaches on a variety of complex indoor and outdoor scenes, demonstrates improved accuracy in both multi-view segmentation and dense reconstruction. This paper demonstrates unsupervised reconstruction of complete temporally coherent 4D scene models with improved non-rigid object segmentation and shape reconstruction and its application to various applications such as free-view rendering and virtual reality.

中文翻译：

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时间相干通用动态场景重建

从多个宽基线相机进行动态场景重建的现有技术主要集中在受控环境中的重建，具有固定校准的相机和强先验约束。本文介绍了一种通用方法，可在不了解场景结构、外观或照明的先验知识的情况下，从多视图宽基线静态或移动相机中获取复杂动态场景的 4D 表示。工作的贡献是：一种用于初始粗重建以初始化联合估计的自动方法；稀疏到密集的时间对应与联合多视图分割和重建相结合，以引入时间连贯性；以及通过引入形状约束对动态场景进行联合分割细化和密集重建的通用鲁棒方法。在各种复杂的室内和室外场景上与最先进的方法进行比较，表明多视图分割和密集重建的准确性有所提高。本文展示了具有改进的非刚性对象分割和形状重建的完整时间相干 4D 场景模型的无监督重建及其在自由视图渲染和虚拟现实等各种应用中的应用。