Computational synthesis planning approaches have achieved recent success in
organic chemistry, where tabulated synthesis procedures are readily available
for supervised learning. The syntheses of inorganic materials, however, exist
primarily as natural language narratives contained within scientific journal
articles. This synthesis information must first be extracted from the text in
order to enable analogous synthesis planning methods for inorganic materials.
In this work, we present a system for automatically extracting structured
representations of synthesis procedures from the texts of materials science
journal articles that describe explicit, experimental syntheses of inorganic
compounds. We define the structured representation as a set of linked events
made up of extracted scientific entities and evaluate two unsupervised
approaches for extracting these structures on expert-annotated articles: a
strong heuristic baseline and a generative model of procedural text. We also
evaluate a variety of supervised models for extracting scientific entities. Our
results provide insight into the nature of the data and directions for further
work in this exciting new area of research.

本文介绍了一种自动从物质科学期刊文章中提取无机化合物实验合成结构化信息的系统，定义为一组链接事件，由提取的科学实体构成，同时评估了两种无监督方法和多种有监督模型。

从材料科学合成程序中自动提取行为图

Automatically Extracting Action Graphs from Materials Science Synthesis  Procedures

The three-dimensional reconstruction of scenes from multiple views has made
impressive strides in recent years, chiefly by methods correlating isolated
feature points, intensities, or curvilinear structure. In the general setting,
i.e., without requiring controlled acquisition, limited number of objects,
abundant patterns on objects, or object curves to follow particular models, the
majority of these methods produce unorganized point clouds, meshes, or voxel
representations of the reconstructed scene, with some exceptions producing 3D
drawings as networks of curves. Many applications, e.g., robotics, urban
planning, industrial design, and hard surface modeling, however, require
structured representations which make explicit 3D curves, surfaces, and their
spatial relationships. Reconstructing surface representations can now be
constrained by the 3D drawing acting like a scaffold to hang on the computed
representations, leading to increased robustness and quality of reconstruction.
This paper presents one way of completing such 3D drawings with surface
reconstructions, by exploring occlusion reasoning through lofting algorithms.

通过挖掘遮挡推理的方式，运用曲面拟合算法，将 3D 绘制曲线转化为曲面，以构建有结构的 3D 重建场景。

通过放样和遮挡推理实现多视角三维绘图

The Surfacing of Multiview 3D Drawings via Lofting and Occlusion  Reasoning

Reconstructing 3D scenes from multiple views has made impressive strides in
recent years, chiefly by correlating isolated feature points, intensity
patterns, or curvilinear structures. In the general setting - without
controlled acquisition, abundant texture, curves and surfaces following
specific models or limiting scene complexity - most methods produce unorganized
point clouds, meshes, or voxel representations, with some exceptions producing
unorganized clouds of 3D curve fragments. Ideally, many applications require
structured representations of curves, surfaces and their spatial relationships.
This paper presents a step in this direction by formulating an approach that
combines 2D image curves into a collection of 3D curves, with topological
connectivity between them represented as a 3D graph. This results in a 3D
drawing, which is complementary to surface representations in the same sense as
a 3D scaffold complements a tent taut over it. We evaluate our results against
truth on synthetic and real datasets.

该论文提出了一种将 2D 图像曲线组合成 3D 曲线集合，构建具有拓扑连接性的 3D 图形的方法，以实现对 3D 场景的结构化表示和重建。