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.

我们提出了一种从小范围环形扫描的全景视频中重建室内外静态场景几何和外表的方法，通过使用球形双八叉树数据结构内的带符号距离场来对优化进行更好的限制，并采用基于广度优先搜索的补充高效树遍历策略进行采样。与先前的固定深度方法相比，该方法能够自适应地在优化过程中对双八叉树进行细分，从而减少了所需体素的数量，并在平均情况下显示出更低的几何误差，尤其是在详细场景中。

OmniSDF：使用全景有向距离函数和自适应双八叉树的场景重建

OmniSDF: Scene Reconstruction using Omnidirectional Signed Distance  Functions and Adaptive Binoctrees

In medieval times, stuccoworkers used a red color, called sinopia, to first
create a sketch of the to-be-made statue on the wall. Today, many of these
statues are destroyed, but using the original drawings, deriving from the red
color also called sinopia, we can reconstruct how the final statue might have
looked.We propose a fully-automated approach to reconstruct a point cloud and
show preliminary results by generating a color-image, a depth-map, as well as
surface normals requiring only a single sketch, and without requiring a
collection of other, similar samples. Our proposed solution allows real-time
reconstruction on-site, for instance, within an exhibition, or to generate a
useful starting point for an expert, trying to manually reconstruct the statue,
all while using only synthetic data for training.

利用以前的红色草图重新构建状态，我们提出了一种完全自动化的方法来重建点云，并通过生成彩色图像、深度图和表面法线来展示初步结果，而无需使用其他相似的样本集。