We present a method to reconstruct indoor and outdoor static scene geometry and appearance from an omnidirectional video moving in a small circular sweep. This setting is challenging because of the small baseline and large depth ranges, making it difficult to find ray crossings. To better constrain the optimization, we estimate geometry as a signed distance field within a spherical binoctree data structure and use a complementary efficient tree traversal strategy based on a breadth-first search for sampling. Unlike regular grids or trees, the shape of this structure well-matches the camera setting, creating a better memory-quality trade-off. From an initial depth estimate, the binoctree is adaptively subdivided throughout the optimization; previous methods use a fixed depth that leaves the scene undersampled. In comparison with three neural optimization methods and two non-neural methods, ours shows decreased geometry error on average, especially in a detailed scene, while significantly reducing the required number of voxels to represent such details.

翻译：我们提出一种方法，用于从在小圆周扫描运动中采集的全向视频中重建室内外静态场景几何与外观。该设定因基线小、深度范围大而具有挑战性，使得寻找光线交叉点变得困难。为更好地约束优化过程，我们将几何估计为球面二叉八叉树数据结构内的符号距离场，并采用基于广度优先搜索的互补高效树遍历策略进行采样。与常规网格或树结构不同，该结构的形状与相机设定良好匹配，从而实现了更优的内存-质量权衡。基于初始深度估计，二叉八叉树在整个优化过程中自适应细分；而先前方法使用固定深度，导致场景采样不足。与三种神经优化方法和两种非神经方法相比，我们的方法在平均几何误差上有所降低（尤其是在细节丰富的场景中），同时显著减少了表示此类细节所需的体素数量。