While initial approaches to Structure-from-Motion (SfM) revolved around both
global and incremental methods, most recent applications rely on incremental
systems to estimate camera poses due to their superior robustness. Though there
has been tremendous progress in SfM `front-ends' powered by deep models learned
from data, the state-of-the-art (incremental) SfM pipelines still rely on
classical SIFT features, developed in 2004. In this work, we investigate
whether leveraging the developments in feature extraction and matching helps
global SfM perform on par with the SOTA incremental SfM approach (COLMAP). To
do so, we design a modular SfM framework that allows us to easily combine
developments in different stages of the SfM pipeline. Our experiments show that
while developments in deep-learning based two-view correspondence estimation do
translate to improvements in point density for scenes reconstructed with global
SfM, none of them outperform SIFT when comparing with incremental SfM results
on a range of datasets. Our SfM system is designed from the ground up to
leverage distributed computation, enabling us to parallelize computation on
multiple machines and scale to large scenes.

通过结合特征提取和匹配方面的发展，我们设计了一个模块化的 SfM 框架，旨在研究全局 SfM 与增量 SfM 方法的性能表现，并且实验证明，在不同数据集上，基于深度学习的两视图对应估计在点密度方面有所提升，但仍不如 SIFT 与增量 SfM 方法相比。

具有深度前端的分布式全局结构恢复

Distributed Global Structure-from-Motion with a Deep Front-End

We investigate an end-to-end method for automatically inducing task-based
dialogue systems from small amounts of unannotated dialogue data. It combines
an incremental semantic grammar - Dynamic Syntax and Type Theory with Records
(DS-TTR) - with Reinforcement Learning (RL), where language generation and
dialogue management are a joint decision problem. The systems thus produced are
incremental: dialogues are processed word-by-word, shown previously to be
essential in supporting natural, spontaneous dialogue. We hypothesised that the
rich linguistic knowledge within the grammar should enable a combinatorially
large number of dialogue variations to be processed, even when trained on very
few dialogues. Our experiments show that our model can process 74% of the
Facebook AI bAbI dataset even when trained on only 0.13% of the data (5
dialogues). It can in addition process 65% of bAbI+, a corpus we created by
systematically adding incremental dialogue phenomena such as restarts and
self-corrections to bAbI. We compare our model with a state-of-the-art
retrieval model, MemN2N. We find that, in terms of semantic accuracy, MemN2N
shows very poor robustness to the bAbI+ transformations even when trained on
the full bAbI dataset.

本研究探讨使用小量未标记数据自动诱导任务型对话系统的端到端方法，利用动态句法和类型理论与记录相结合的增量语义语法和强化学习，其产生的系统是增量的，可以处理 74％的 Facebook AI bAbI 数据集，和 65％的 bAbI + 语料库，并比较了当前最先进的检索模型 MemN2N，揭示出其对 bAbI + 的转换的性能非常差。