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

We propose a novel-view augmentation (NOVA) strategy to train NeRFs for
photo-realistic 3D composition of dynamic objects in a static scene. Compared
to prior work, our framework significantly reduces blending artifacts when
inserting multiple dynamic objects into a 3D scene at novel views and times;
achieves comparable PSNR without the need for additional ground truth
modalities like optical flow; and overall provides ease, flexibility, and
scalability in neural composition. Our codebase is on GitHub.

提出一种名为 NOVA 的新视角增强策略，用于训练具有光真实 3D 组成的动态对象在静态场景中。相比于先前的工作，我们的框架在将多个动态对象插入到新视角和时间的 3D 场景中时显著减少了混合伪影，实现了可比的峰值信噪比，无需像光流那样的附加真实模态，并在神经组成方面提供了便捷性，灵活性和可伸缩性。我们的代码库托管在 GitHub 上。

NOVA：用于神经动态对象组合的新视图增强

NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects

Inspired by the recent success of application of dense data approach by using
ORB-SLAM and RGB-D SLAM, we propose a better pipeline of real-time SLAM in
dynamics environment. Different from previous SLAM which can only handle static
scenes, we are presenting a solution which use RGB-D SLAM as well as YOLO
real-time object detection to segment and remove dynamic scene and then
construct static scene 3D. We gathered a dataset which allows us to jointly
consider semantics, geometry, and physics and thus enables us to reconstruct
the static scene while filtering out all dynamic objects.

本文提出了一个新的实时 SLAM 流水线，使用 RGB-D SLAM 和 YOLO 实时物体检测来分割和去除动态场景，并构建静态场景 3D 模型。