In contrast to sparse keypoints, a handful of line segments can concisely
encode the high-level scene layout, as they often delineate the main structural
elements. In addition to offering strong geometric cues, they are also
omnipresent in urban landscapes and indoor scenes. Despite their apparent
advantages, current line-based reconstruction methods are far behind their
point-based counterparts. In this paper we aim to close the gap by introducing
LIMAP, a library for 3D line mapping that robustly and efficiently creates 3D
line maps from multi-view imagery. This is achieved through revisiting the
degeneracy problem of line triangulation, carefully crafted scoring and track
building, and exploiting structural priors such as line coincidence,
parallelism, and orthogonality. Our code integrates seamlessly with existing
point-based Structure-from-Motion methods and can leverage their 3D points to
further improve the line reconstruction. Furthermore, as a byproduct, the
method is able to recover 3D association graphs between lines and points /
vanishing points (VPs). In thorough experiments, we show that LIMAP
significantly outperforms existing approaches for 3D line mapping. Our robust
3D line maps also open up new research directions. We show two example
applications: visual localization and bundle adjustment, where integrating
lines alongside points yields the best results. Code is available at
this https URL

本文介绍了一种名为 LIMAP 的库，用于从多视角图像中稳健高效地创建 3D 线路图，该库通过重新审视线三角化的退化问题、精心制作的得分和跟踪构建以及利用线 / 点的结构先验知识来实现。在全面实验中，我们展示了 LIMAP 在 3D 线路图上显着优于现有方法，并且我们的稳健 3D 线路图也开辟了新的研究方向：视觉本地化和束调整。

3D 线条映射再探讨

3D Line Mapping Revisited

This study aims to enable more reliable automated post-disaster building
damage classification using artificial intelligence (AI) and multi-view
imagery. The current practices and research efforts in adopting AI for
post-disaster damage assessment are generally (a) qualitative, lacking refined
classification of building damage levels based on standard damage scales, and
(b) trained based on aerial or satellite imagery with limited views, which,
although indicative, are not completely descriptive of the damage scale. To
enable more accurate and reliable automated quantification of damage levels,
the present study proposes the use of more comprehensive visual data in the
form of multiple ground and aerial views of the buildings. To have such a
spatially-aware damage prediction model, a Multi-view Convolution Neural
Network (MV-CNN) architecture is used that combines the information from
different views of a damaged building. This spatial 3D context damage
information will result in more accurate identification of damages and reliable
quantification of damage levels. The proposed model is trained and validated on
reconnaissance visual dataset containing expert-labeled, geotagged images of
the inspected buildings following hurricane Harvey. The developed model
demonstrates reasonably good accuracy in predicting the damage levels and can
be used to support more informed and reliable AI-assisted disaster management
practices.

探索使用多视角图像和 AI 实现可靠的灾后建筑损伤分类方法，提出基于多视角卷积神经网络结构的损伤预测模型可以提高灾后建筑损伤等级的准确度和可靠性。

基于多视角深度学习的可靠后灾害损伤分类

Multi-view deep learning for reliable post-disaster damage classification

Modern 3D human pose estimation techniques rely on deep networks, which
require large amounts of training data. While weakly-supervised methods require
less supervision, by utilizing 2D poses or multi-view imagery without
annotations, they still need a sufficiently large set of samples with 3D
annotations for learning to succeed.
In this paper, we propose to overcome this problem by learning a
geometry-aware body representation from multi-view images without annotations.
To this end, we use an encoder-decoder that predicts an image from one
viewpoint given an image from another viewpoint. Because this representation
encodes 3D geometry, using it in a semi-supervised setting makes it easier to
learn a mapping from it to 3D human pose. As evidenced by our experiments, our
approach significantly outperforms fully-supervised methods given the same
amount of labeled data, and improves over other semi-supervised methods while
using as little as 1% of the labeled data.

本文提出使用多视角图像而无需注释，来学习一个具有几何感知能力的身体表示，通过使用该身体表示，我们的方法在仅使用少量标记数据的情况下，显著优于完全监督的方法，并且在使用仅 1% 标记数据时，也优于其他半监督方法。