We address 2D floorplan reconstruction from 3D scans. Existing approaches
typically employ heuristically designed multi-stage pipelines. Instead, we
formulate floorplan reconstruction as a single-stage structured prediction
task: find a variable-size set of polygons, which in turn are variable-length
sequences of ordered vertices. To solve it we develop a novel Transformer
architecture that generates polygons of multiple rooms in parallel, in a
holistic manner without hand-crafted intermediate stages. The model features
two-level queries for polygons and corners, and includes polygon matching to
make the network end-to-end trainable. Our method achieves a new
state-of-the-art for two challenging datasets, Structured3D and SceneCAD, along
with significantly faster inference than previous methods. Moreover, it can
readily be extended to predict additional information, i.e., semantic room
types and architectural elements like doors and windows. Our code and models
are available at: this https URL

该研究主要讨论了如何使用 Transformer Architecture 来解决 2D 平面图的重建问题，并通过多个数据集的实验证明了其在各方面的优越性。

连接点：使用双层查询进行平面图重建

Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries

We study the cross-entropy method (CEM) for the non-convex optimization of a
continuous and parameterized objective function and introduce a differentiable
variant that enables us to differentiate the output of CEM with respect to the
objective function's parameters. In the machine learning setting this brings
CEM inside of the end-to-end learning pipeline where this has otherwise been
impossible. We show applications in a synthetic energy-based structured
prediction task and in non-convex continuous control. In the control setting we
show how to embed optimal action sequences into a lower-dimensional space. DCEM
enables us to fine-tune CEM-based controllers with policy optimization.

本文研究交叉熵方法在非凸优化中的应用，并提出了一种可微分的变体。在应用上，我们在合成能源预测任务和非凸连续控制中展示了其效果。特别是，我们展示了如何将最优的行动序列嵌入到一个低维空间中进行控制，从而通过策略优化来对基于 CEM 的控制器进行微调。

可微交叉熵方法

The Differentiable Cross-Entropy Method

In this paper, we propose to learn a Mahalanobis distance to perform
alignment of multivariate time series. The learning examples for this task are
time series for which the true alignment is known. We cast the alignment
problem as a structured prediction task, and propose realistic losses between
alignments for which the optimization is tractable. We provide experiments on
real data in the audio to audio context, where we show that the learning of a
similarity measure leads to improvements in the performance of the alignment
task. We also propose to use this metric learning framework to perform feature
selection and, from basic audio features, build a combination of these with
better performance for the alignment.

本文旨在提出一种学习马氏距离来执行多变量时间序列对齐的方法，并将对齐问题作为结构预测任务来解决，提出了可行的优化方法在真实数据上进行实验证明此方法能够有效地提升对齐性能，并在基本音频特征的基础上提供了一种构建更好性能的特征组合的方法。