Human modelling and pose estimation stands at the crossroads of Computer
Vision, Computer Graphics, and Machine Learning. This paper presents a thorough
investigation of this interdisciplinary field, examining various algorithms,
methodologies, and practical applications. It explores the diverse range of
sensor technologies relevant to this domain and delves into a wide array of
application areas. Additionally, we discuss the challenges and advancements in
2D and 3D human modelling methodologies, along with popular datasets, metrics,
and future research directions. The main contribution of this paper lies in its
up-to-date comparison of state-of-the-art (SOTA) human pose estimation
algorithms in both 2D and 3D domains. By providing this comprehensive overview,
the paper aims to enhance understanding of 3D human modelling and pose
estimation, offering insights into current SOTA achievements, challenges, and
future prospects within the field.

该论文探讨了人体建模和姿态估计的交叉领域，涉及计算机视觉、计算机图形学和机器学习，包括算法、方法学和实际应用，以及相关的传感器技术、2D 和 3D 人体建模方法学的挑战和进展，常用数据集和度量标准，以及未来的研究方向。该论文的主要贡献在于对 2D 和 3D 领域最先进的人体姿态估计算法的全面比较，旨在增进对 3D 人体建模和姿态估计的理解，并提供当前最先进成果、挑战和未来前景的见解。

人体建模与姿态估计概述

Human Modelling and Pose Estimation Overview

This article presents a comprehensive review of and analysis of
state-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle)
applications, focusing on canopy-level and high-speed scenarios. This article
presents a comprehensive exploration of sensor technologies suitable for UAV
mapping, assessing their capabilities to provide measurements that meet the
requirements of fast UAV mapping. Furthermore, the study conducts extensive
experiments in a simulated environment to evaluate the performance of three
distinct mapping algorithms: Direct Sparse Odometry (DSO), Stereo DSO (SDSO),
and DSO Lite (DSOL). The experiments delve into mapping accuracy and mapping
speed, providing valuable insights into the strengths and limitations of each
algorithm. The results highlight the versatility and shortcomings of these
algorithms in meeting the demands of modern UAV applications. The findings
contribute to a nuanced understanding of UAV mapping dynamics, emphasizing
their applicability in complex environments and high-speed scenarios. This
research not only serves as a benchmark for mapping algorithm comparisons but
also offers practical guidance for selecting sensors tailored to specific UAV
mapping applications.

对无人机应用中基于林冠和高速场景的最新地图算法进行全面评估和分析，以及对适用于无人机地图绘制的传感器技术进行全面探索和评估，通过模拟环境进行广泛实验来评估三种不同的地图算法，即直接稀疏视觉里程计（DSO），双目 DSO（SDSO）和轻量级 DSO（DSOL）的性能，并突出了这些算法的优劣势以及它们在现代无人机应用中的适用性。研究结果旨在为地图算法比较提供基准，并为选择针对特定无人机地图绘制应用定制传感器提供实际指导。

无人机携带的林冠层和高速飞行无人机应用的地图算法

UAV-borne Mapping Algorithms for Canopy-Level and High-Speed Drone  Applications

In response to the increasing global demand for food, feed, fiber, and fuel,
digital agriculture is rapidly evolving to meet these demands while reducing
environmental impact. This evolution involves incorporating data science,
machine learning, sensor technologies, robotics, and new management strategies
to establish a more sustainable agricultural framework. So far, machine
learning research in digital agriculture has predominantly focused on
model-centric approaches, focusing on model design and evaluation. These
efforts aim to optimize model accuracy and efficiency, often treating data as a
static benchmark. Despite the availability of agricultural data and
methodological advancements, a saturation point has been reached, with many
established machine learning methods achieving comparable levels of accuracy
and facing similar limitations. To fully realize the potential of digital
agriculture, it is crucial to have a comprehensive understanding of the role of
data in the field and to adopt data-centric machine learning. This involves
developing strategies to acquire and curate valuable data and implementing
effective learning and evaluation strategies that utilize the intrinsic value
of data. This approach has the potential to create accurate, generalizable, and
adaptable machine learning methods that effectively and sustainably address
agricultural tasks such as yield prediction, weed detection, and early disease
identification

数字农业通过数据科学、机器学习、传感技术、机器人技术和新的管理策略，在减少环境影响的同时满足全球对食物、饲料、纤维和燃料不断增长的需求。为了充分发挥数字农业的潜力，强调数据积极性的机器学习研究在此领域显得至关重要，需要发展获取和整理有价值数据的策略，并实施能够利用数据内在价值的有效学习和评估策略，从而创建准确、可推广和适应性强的机器学习方法，有效可持续地应对农业任务，如产量预测、杂草检测和早期病害识别。