Symbolic Aggregate approXimation (SAX) is a common dimensionality reduction
approach for time-series data which has been employed in a variety of domains,
including classification and anomaly detection in time-series data. Domains
also include shape recognition where the shape outline is converted into
time-series data forinstance epoch classification of archived arrowheads. In
this paper we propose a dimensionality reduction and shape recognition approach
based on the SAX algorithm, an application which requires responses on cost
efficient, IoT-like, platforms. The challenge is largely dealing with the
computational expense of the SAX algorithm in IoT-like applications, from
simple time-series dimension reduction through shape recognition. The approach
is based on lowering the dimensional space while capturing and preserving the
most representative features of the shape. We present three scenarios of
increasing computational complexity backing up our statements with measurement
of performance characteristics

基于 SAX 算法，本文提出了一种降维和形状识别方法，旨在解决物联网等应用中 SAX 算法的计算开销问题，通过降低维度空间的同时捕获和保留形状的最具代表性的特征，实验证明了这种方法在性能特性方面的优势。

物联网应用中符号聚合近似的性能评估

Performance Examination of Symbolic Aggregate Approximation in IoT  Applications

Early work in computer vision considered a host of geometric cues for both
shape reconstruction and recognition. However, since then, the vision community
has focused heavily on shading cues for reconstruction, and moved towards
data-driven approaches for recognition. In this paper, we reconsider these
perhaps overlooked "boundary" cues (such as self occlusions and folds in a
surface), as well as many other established constraints for shape
reconstruction. In a variety of user studies and quantitative tasks, we
evaluate how well these cues inform shape reconstruction (relative to each
other) in terms of both shape quality and shape recognition. Our findings
suggest many new directions for future research in shape reconstruction, such
as automatic boundary cue detection and relaxing assumptions in shape from
shading (e.g. orthographic projection, Lambertian surfaces).

该论文重新考虑了在形状重建中可能被忽视的 “边界” 线索以及其他已建立的约束条件，并通过多项用户研究和定量任务评估了这些线索相对于彼此的信息量，结果表明这些线索在未来的形状重建研究中提供了很多新的方向。

三维物体形状恢复的边界线索

Boundary Cues for 3D Object Shape Recovery

We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed onto them by Kd-trees. Unlike the currently dominant
convolutional architectures that usually require rasterization on uniform
two-dimensional or three-dimensional grids, Kd-networks do not rely on such
grids in any way and therefore avoid poor scaling behaviour. In a series of
experiments with popular shape recognition benchmarks, Kd-networks demonstrate
competitive performance in a number of shape recognition tasks such as shape
classification, shape retrieval and shape part segmentation.

本文介绍了一种新的深度学习架构 (Kd-network)，适用于 3D 模型识别任务，可以处理非结构化点云数据，在常用的形状分类、检索和部分分割任务中表现出了竞争性的性能。