This paper presents an explore-and-classify framework for structured
architectural reconstruction from an aerial image. Starting from a potentially
imperfect building reconstruction by an existing algorithm, our approach 1)
explores the space of building models by modifying the reconstruction via
heuristic actions; 2) learns to classify the correctness of building models
while generating classification labels based on the ground-truth, and 3)
repeat. At test time, we iterate exploration and classification, seeking for a
result with the best classification score. We evaluate the approach using
initial reconstructions by two baselines and two state-of-the-art
reconstruction algorithms. Qualitative and quantitative evaluations demonstrate
that our approach consistently improves the reconstruction quality from every
initial reconstruction.

该论文提出了一个探索和分类框架，用于从航拍图像中进行结构化建筑重建，通过启发式行动修改重建模型，学习分类建筑模型的正确性，最终改进了每个初始重建的重建质量。

通过勘探和分类进行结构化室外建筑重建

Structured Outdoor Architecture Reconstruction by Exploration and  Classification

Consider a general machine learning setting where the output is a set of
labels or sequences. This output set is unordered and its size varies with the
input. Whereas multi-label classification methods seem a natural first resort,
they are not readily applicable to set-valued outputs because of the growth
rate of the output space; and because conventional sequence generation doesn't
reflect sets' order-free nature. In this paper, we propose a unified
framework--sequential set generation (SSG)--that can handle output sets of
labels and sequences. SSG is a meta-algorithm that leverages any probabilistic
learning method for label or sequence prediction, but employs a proper
regularization such that a new label or sequence is generated repeatedly until
the full set is produced. Though SSG is sequential in nature, it does not
penalize the ordering of the appearance of the set elements and can be applied
to a variety of set output problems, such as a set of classification labels or
sequences. We perform experiments with both benchmark and synthetic data sets
and demonstrate SSG's strong performance over baseline methods.

本文提出了一种新的机器学习框架 —— 顺序集合生成 (SSG)，可以处理标签和序列输出集。通过采用适当正则化和循环生成新标签或序列以生成完整集合，SSG 在性质上顺序不敏感且适用于各种集合输出问题，如分类标签或序列。实验结果表明，SSG 对于基准方法具有良好的性能。