Many practical systems for image-based surface reconstruction employ a
stereo/multi-stereo paradigm, due to its ability to scale for large scenes and
its ease of implementation for out-of-core operations. In this process,
multiple and abundant depth maps from stereo matching must be combined and
fused into a single, consistent, and clean point cloud. However, the noises and
outliers caused by stereo matching and the heterogenous geometric errors of the
poses present a challenge for existing fusion algorithms, since they mostly
assume Gaussian errors and predict fused results based on data from local
spatial neighborhoods, which may inherit uncertainties from multiple depths
resulting in lowered accuracy. In this paper, we propose a novel depth fusion
paradigm, that instead of numerically fusing points from multiple depth maps,
selects the best depth map per point, and combines them into a single and clean
point cloud. This paradigm, called select-and-combine (SAC), is achieved
through modeling the point level fusion using local Markov Netlets, a
micro-network over point across neighboring views for depth/view selection,
followed by a Netlets collapse process for point combination. The Markov
Netlets are optimized such that they can inherently leverage spatial
consistencies among depth maps of neighboring views, thus they can address
errors beyond Gaussian ones. Our experiment results show that our approach
outperforms existing depth fusion approaches by increasing the F1 score that
considers both accuracy and completeness by 2.07% compared to the best existing
method. Finally, our approach generates clearer point clouds that are 18% less
redundant while with a higher accuracy before fusion

通过使用局部 Markov Netlets 对选择性合并（SAC）方法，从多个深度图中选择最佳深度图，并将其组合成单个干净的点云进行深度融合，以提高精度和完整性，相比最佳现有方法，我们的方法将 F1 分数提高了 2.07％，并生成了 18％更清晰且更准确的点云。

选择与合并（SAC）：一种通过高效的局部马尔可夫网络实现点云生成的新型多视深度融合算法

Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm  for Point Cloud Generation via Efficient Local Markov Netlets

The efficient fusion of depth maps is a key part of most state-of-the-art 3D
reconstruction methods. Besides requiring high accuracy, these depth fusion
methods need to be scalable and real-time capable. To this end, we present a
novel real-time capable machine learning-based method for depth map fusion.
Similar to the seminal depth map fusion approach by Curless and Levoy, we only
update a local group of voxels to ensure real-time capability. Instead of a
simple linear fusion of depth information, we propose a neural network that
predicts non-linear updates to better account for typical fusion errors. Our
network is composed of a 2D depth routing network and a 3D depth fusion network
which efficiently handle sensor-specific noise and outliers. This is especially
useful for surface edges and thin objects for which the original approach
suffers from thickening artifacts. Our method outperforms the traditional
fusion approach and related learned approaches on both synthetic and real data.
We demonstrate the performance of our method in reconstructing fine geometric
details from noise and outlier contaminated data on various scenes.

本文提出了一种基于机器学习的实时深度图融合方法，使用 2D 深度路由网络和 3D 深度融合网络来预测非线性更新，处理传感器特定噪声和异常值，能更好地避免传统方法中的粗化伪影问题，在合成数据和真实数据中均取得了优于传统方法和相关方法的结果。

RoutedFusion：学习实时深度地图融合

RoutedFusion: Learning Real-time Depth Map Fusion

In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and provides great results, it is not able to reconstruct (partially)
occluded surfaces and requires a large number frames to filter out sensor noise
and outliers. Motivated by the availability of large 3D model repositories and
recent advances in deep learning, we present a novel 3D CNN architecture that
learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric
fusion approach in terms of noise reduction and outlier suppression. By
learning the structure of real world 3D objects and scenes, our approach is
further able to reconstruct occluded regions and to fill in gaps in the
reconstruction. We demonstrate that our learning based approach outperforms
both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric
fusion. Further, we demonstrate state-of-the-art 3D shape completion results.

本研究提出了一种基于深度学习的方法来进行深度融合，可以更好的处理深度图像数据中的噪声和异常值，并能够完整地重建三维物体形态，并且能够填补穴洞，具有更好的性能表现。