Indoor scenes we are living in are visually homogenous or textureless, while
they inherently have structural forms and provide enough structural priors for
3D scene reconstruction. Motivated by this fact, we propose a structure-aware
online signed distance fields (SDF) reconstruction framework in indoor scenes,
especially under the Atlanta world (AW) assumption. Thus, we dub this
incremental SDF reconstruction for AW as AiSDF. Within the online framework, we
infer the underlying Atlanta structure of a given scene and then estimate
planar surfel regions supporting the Atlanta structure. This Atlanta-aware
surfel representation provides an explicit planar map for a given scene. In
addition, based on these Atlanta planar surfel regions, we adaptively sample
and constrain the structural regularity in the SDF reconstruction, which
enables us to improve the reconstruction quality by maintaining a high-level
structure while enhancing the details of a given scene. We evaluate the
proposed AiSDF on the ScanNet and ReplicaCAD datasets, where we demonstrate
that the proposed framework is capable of reconstructing fine details of
objects implicitly, as well as structures explicitly in room-scale scenes.

在室内场景中，我们提出了一种基于结构感知的在线有符号距离场（SDF）重建框架，特别是在 Atlanta 世界（AW）假设下，命名为 AiSDF。通过该在线框架，我们推断给定场景的潜在 Atlanta 结构，然后估计支撑 Atlanta 结构的平面 surfel 区域，提供了给定场景的显式平面地图。此外，基于这些 Atlanta 平面 surfel 区域，我们对 SDF 重建进行自适应采样和约束结构规律性，从而在保持高层结构的同时增强给定场景的细节。我们在 ScanNet 和 ReplicaCAD 数据集上评估了提出的 AiSDF，证明了该框架能够隐式地重建对象的细节，并在房间尺度的场景中显式地重建结构。

AiSDF: 室内场景中结构感知的神经有符号距离场

AiSDF: Structure-aware Neural Signed Distance Fields in Indoor Scenes

We propose a novel visual-inertial odometry approach that adopts structural
regularity in man-made environments. Instead of using Manhattan world
assumption, we use Atlanta world model to describe such regularity. An Atlanta
world is a world that contains multiple local Manhattan worlds with different
heading directions. Each local Manhattan world is detected on-the-fly, and
their headings are gradually refined by the state estimator when new
observations are coming. With fully exploration of structural lines that
aligned with each local Manhattan worlds, our visual-inertial odometry method
become more accurate and robust, as well as much more flexible to different
kinds of complex man-made environments. Through extensive benchmark tests and
real-world tests, the results show that the proposed approach outperforms
existing visual-inertial systems in large-scale man-made environments

提出了一种新的视觉惯性测距方法，采用 Atlanta World 模型，而不是 Manhattan World 假设，探索了与每个本地 Manhattan World 对齐的结构线，提高了测距方法的准确性和鲁棒性，并在大规模人造环境中表现出相对于现有视觉惯性系统更好的性能。