  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.
