Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy

Google Earth (GE) has recently become the focus of increasing interest and popularity among available online virtual globes used in scientific research projects, due to the free and easily accessed satellite imagery provided with global coverage. Nevertheless, the uses of this service raises several research questions on the quality and uncertainty of spatial data (e.g. positional accuracy, precision, consistency), with implications for potential uses like data collection and validation. This paper aims to analyze the horizontal accuracy of very high resolution (VHR) GE images in the city of Rome (Italy) for the years 2007, 2011, and 2013. The evaluation was conducted by using both Global Positioning System ground truth data and cadastral photogrammetric vertex as independent check points. The validation process includes the comparison of histograms, graph plots, tests of normality, azimuthal direction errors, and the calculation of standard statistical parameters. The results show that GE VHR imageries of Rome have an overall positional accuracy close to 1 m, sufficient for deriving ground truth samples, measurements, and large-scale planimetric maps

단일 이미지로부터 여러 사람의 표현적 전신 3D 자세 및 형태 추정

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. 이경무.Human is the most centric and interesting object in our life: many human-centric techniques and studies have been proposed from both industry and academia, such as motion capture and human-computer interaction. Recovery of accurate 3D geometry of human (i.e., 3D human pose and shape) is a key component of the human-centric techniques and studies. With the rapid spread of cameras, a single RGB image has become a popular input, and many single RGB-based 3D human pose and shape estimation methods have been proposed. The 3D pose and shape of the whole body, which includes hands and face, provides expressive and rich information, including human intention and feeling. Unfortunately, recovering the whole-body 3D pose and shape is greatly challenging; thus, it has been attempted by few works, called expressive methods. Instead of directly solving the expressive 3D pose and shape estimation, the literature has been developed for recovery of the 3D pose and shape of each part (i.e., body, hands, and face) separately, called part-specific methods. There are several more simplifications. For example, many works estimate only 3D pose without shape because additional 3D shape estimation makes the problem much harder. In addition, most works assume a single person case and do not consider a multi-person case. Therefore, there are several ways to categorize current literature; 1) part-specific methods and expressive methods, 2) 3D human pose estimation methods and 3D human pose and shape estimation methods, and 3) methods for a single person and methods for multiple persons. The difficulty increases while the outputs of methods become richer by changing from part-specific to expressive, from 3D pose estimation to 3D pose and shape estimation, and from a single person case to multi-person case. This dissertation introduces three approaches towards expressive 3D multi-person pose and shape estimation from a single image; thus, the output can finally provide the richest information. The first approach is for 3D multi-person body pose estimation, the second one is 3D multi-person body pose and shape estimation, and the final one is expressive 3D multi-person pose and shape estimation. Each approach tackles critical limitations of previous state-of-the-art methods, thus bringing the literature closer to the real-world environment. First, a 3D multi-person body pose estimation framework is introduced. In contrast to the single person case, the multi-person case additionally requires camera-relative 3D positions of the persons. Estimating the camera-relative 3D position from a single image involves high depth ambiguity. The proposed framework utilizes a deep image feature with the camera pinhole model to recover the camera-relative 3D position. The proposed framework can be combined with any 3D single person pose and shape estimation methods for 3D multi-person pose and shape. Therefore, the following two approaches focus on the single person case and can be easily extended to the multi-person case by using the framework of the first approach. Second, a 3D multi-person body pose and shape estimation method is introduced. It extends the first approach to additionally predict accurate 3D shape while its accuracy significantly outperforms previous state-of-the-art methods by proposing a new target representation, lixel-based 1D heatmap. Finally, an expressive 3D multi-person pose and shape estimation method is introduced. It integrates the part-specific 3D pose and shape of the above approaches; thus, it can provide expressive 3D human pose and shape. In addition, it boosts the accuracy of the estimated 3D pose and shape by proposing a 3D positional pose-guided 3D rotational pose prediction system. The proposed approaches successfully overcome the limitations of the previous state-of-the-art methods. The extensive experimental results demonstrate the superiority of the proposed approaches in both qualitative and quantitative ways.인간은 우리의 일상생활에서 가장 중심이 되고 흥미로운 대상이다. 그에 따라 모션 캡처, 인간-컴퓨터 인터렉션 등 많은 인간중심의 기술과 학문이 산업계와 학계에서 제안되었다. 인간의 정확한 3D 기하 (즉, 인간의 3D 자세와 형태)를 복원하는 것은 인간중심 기술과 학문에서 가장 중요한 부분 중 하나이다. 카메라의 빠른 대중화로 인해 단일 이미지는 많은 알고리즘의 널리 쓰이는 입력이 되었고, 그로 인해 많은 단일 이미지 기반의 3D 인간 자세 및 형태 추정 알고리즘이 제안되었다. 손과 발을 포함한 전신의 3D 자세와 형태는 인간의 의도와 느낌을 포함한 표현적이고 풍부한 정보를 제공한다. 하지만 전신의 3D 자세와 형태를 복원하는 것은 매우 어렵기 때문에 오직 극소수의 방법만이 이를 풀기 위해 제안되었고, 이를 위한 방법들을 표현적인 방법이라고 부른다. 표현적인 3D 자세와 형태를 한 번에 복원하는 것 대신, 사람의 몸, 손, 그리고 얼굴의 3D 자세와 형태를 따로 복원하는 방법들이 제안되었다. 이러한 방법들을 부분 특유 방법이라고 부른다. 이러한 문제의 간단화 이외에도 몇 가지의 간단화가 더 존재한다. 예를 들어, 많은 방법은 3D 형태를 제외한 3D 자세만을 추정한다. 이는 추가적인 3D 형태 추정이 문제를 더 어렵게 만들기 때문이다. 또한, 대부분의 방법은 오직 단일 사람의 경우만 고려하고 여러 사람의 경우는 고려하지 않는다. 그러므로, 현재 제안된 방법들은 몇 가지 기준에 의해 분류될 수 있다; 1) 부분 특유 방법 vs. 표현적 방법, 2) 3D 자세 추정 방법 vs. 3D 자세 및 형태 추정 방법, 그리고 3) 단일 사람을 위한 방법 vs. 여러 사람을 위한 방법. 부분 특유에서 표현적으로, 3D 자세 추정에서 3D 자세 및 형태 추정으로, 단일 사람에서 여러 사람으로 갈수록 추정이 더 어려워지지만, 더 풍부한 정보를 출력할 수 있게 된다. 본 학위논문은 단일 이미지로부터 여러 사람의 표현적인 3D 자세 및 형태 추정을 향하는 세 가지의 접근법을 소개한다. 따라서 최종적으로 제안된 방법은 가장 풍부한 정보를 제공할 수 있다. 첫 번째 접근법은 여러 사람을 위한 3D 자세 추정이고, 두 번째는 여러 사람을 위한 3D 자세 및 형태 추정이고, 그리고 마지막은 여러 사람을 위한 표현적인 3D 자세 및 형태 추정을 위한 방법이다. 각 접근법은 기존 방법들이 가진 중요한 한계점들을 해결하여 제안된 방법들이 실생활에서 쓰일 수 있도록 한다. 첫 번째 접근법은 여러 사람을 위한 3D 자세 추정 프레임워크이다. 단일 사람의 경우와는 다르게 여러 사람의 경우 사람마다 카메라 상대적인 3D 위치가 필요하다. 카메라 상대적인 3D 위치를 단일 이미지로부터 추정하는 것은 매우 높은 깊이 모호성을 동반한다. 제안하는 프레임워크는 심층 이미지 피쳐와 카메라 핀홀 모델을 사용하여 카메라 상대적인 3D 위치를 복원한다. 이 프레임워크는 어떤 단일 사람을 위한 3D 자세 및 형태 추정 방법과 합쳐질 수 있기 때문에, 다음에 소개될 두 접근법은 오직 단일 사람을 위한 3D 자세 및 형태 추정에 초점을 맞춘다. 다음에 소개될 두 접근법에서 제안된 단일 사람을 위한 방법들은 첫 번째 접근법에서 소개되는 여러 사람을 위한 프레임워크를 사용하여 쉽게 여러 사람의 경우로 확장할 수 있다. 두 번째 접근법은 여러 사람을 위한 3D 자세 및 형태 추정 방법이다. 이 방법은 첫 번째 접근법을 확장하여 정확도를 유지하면서 추가로 3D 형태를 추정하게 한다. 높은 정확도를 위해 릭셀 기반의 1D 히트맵을 제안하고, 이로 인해 기존에 발표된 방법들보다 큰 폭으로 높은 성능을 얻는다. 마지막 접근법은 여러 사람을 위한 표현적인 3D 자세 및 형태 추정 방법이다. 이것은 몸, 손, 그리고 얼굴마다 3D 자세 및 형태를 하나로 통합하여 표현적인 3D 자세 및 형태를 얻는다. 게다가, 이것은 3D 위치 포즈 기반의 3D 회전 포즈 추정기법을 제안함으로써 기존에 발표된 방법들보다 훨씬 높은 성능을 얻는다. 제안된 접근법들은 기존에 발표되었던 방법들이 갖는 한계점들을 성공적으로 극복한다. 광범위한 실험적 결과가 정성적, 정량적으로 제안하는 방법들의 효용성을 보여준다.1 Introduction 1 1.1 Background and Research Issues 1 1.2 Outline of the Dissertation 3 2 3D Multi-Person Pose Estimation 7 2.1 Introduction 7 2.2 Related works 10 2.3 Overview of the proposed model 13 2.4 DetectNet 13 2.5 PoseNet 14 2.5.1 Model design 14 2.5.2 Loss function 14 2.6 RootNet 15 2.6.1 Model design 15 2.6.2 Camera normalization 19 2.6.3 Network architecture 19 2.6.4 Loss function 20 2.7 Implementation details 20 2.8 Experiment 21 2.8.1 Dataset and evaluation metric 21 2.8.2 Experimental protocol 22 2.8.3 Ablation study 23 2.8.4 Comparison with state-of-the-art methods 25 2.8.5 Running time of the proposed framework 31 2.8.6 Qualitative results 31 2.9 Conclusion 34 3 3D Multi-Person Pose and Shape Estimation 35 3.1 Introduction 35 3.2 Related works 38 3.3 I2L-MeshNet 41 3.3.1 PoseNet 41 3.3.2 MeshNet 43 3.3.3 Final 3D human pose and mesh 45 3.3.4 Loss functions 45 3.4 Implementation details 47 3.5 Experiment 48 3.5.1 Datasets and evaluation metrics 48 3.5.2 Ablation study 50 3.5.3 Comparison with state-of-the-art methods 57 3.6 Conclusion 60 4 Expressive 3D Multi-Person Pose and Shape Estimation 63 4.1 Introduction 63 4.2 Related works 66 4.3 Pose2Pose 69 4.3.1 PositionNet 69 4.3.2 RotationNet 70 4.4 Expressive 3D human pose and mesh estimation 72 4.4.1 Body part 72 4.4.2 Hand part 73 4.4.3 Face part 73 4.4.4 Training the networks 74 4.4.5 Integration of all parts in the testing stage 74 4.5 Implementation details 77 4.6 Experiment 78 4.6.1 Training sets and evaluation metrics 78 4.6.2 Ablation study 78 4.6.3 Comparison with state-of-the-art methods 82 4.6.4 Running time 87 4.7 Conclusion 87 5 Conclusion and Future Work 89 5.1 Summary and Contributions of the Dissertation 89 5.2 Future Directions 90 5.2.1 Global Context-Aware 3D Multi-Person Pose Estimation 91 5.2.2 Unied Framework for Expressive 3D Human Pose and Shape Estimation 91 5.2.3 Enhancing Appearance Diversity of Images Captured from Multi-View Studio 92 5.2.4 Extension to the video for temporally consistent estimation 94 5.2.5 3D clothed human shape estimation in the wild 94 5.2.6 Robust human action recognition from a video 96 Bibliography 98 국문초록 111Docto

Code Prediction by Feeding Trees to Transformers

We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems. First, we report that using the recently proposed Transformer architecture even out-of-the-box outperforms previous neural and non-neural systems for code prediction. We then show that by making the Transformer architecture aware of the syntactic structure of code, we further increase the margin by which a Transformer-based system outperforms previous systems. With this, it outperforms the accuracy of an RNN-based system (similar to Hellendoorn et al. 2018) by 18.3\%, the Deep3 system (Raychev et al 2016) by 14.1\%, and an adaptation of Code2Seq (Alon et al., 2018) for code prediction by 14.4\%. We present in the paper several ways of communicating the code structure to the Transformer, which is fundamentally built for processing sequence data. We provide a comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Facebook internal Python corpus. Our code and data preparation pipeline will be available in open source

Hybrid Architectures for Object Pose and Velocity Tracking at the Intersection of Kalman Filtering and Machine Learning

The study of object perception algorithms is fundamental for the development of robotic platforms capable of planning and executing actions involving objects with high precision, reliability and safety. Indeed, this topic has been vastly explored in both the robotic and computer vision research communities using diverse techniques, ranging from classical Bayesian filtering to more modern Machine Learning techniques, and complementary sensing modalities such as vision and touch. Recently, the ever-growing availability of tools for synthetic data generation has substantially increased the adoption of Deep Learning for both 2D tasks, as object detection and segmentation, and 6D tasks, such as object pose estimation and tracking. The proposed methods exhibit interesting performance on computer vision benchmarks and robotic tasks, e.g. using object pose estimation for grasp planning purposes. Nonetheless, they generally do not consider useful information connected with the physics of the object motion and the peculiarities and requirements of robotic systems. Examples are the necessity to provide well-behaved output signals for robot motion control, the possibility to integrate modelling priors on the motion of the object and algorithmic priors. These help exploit the temporal correlation of the object poses, handle the pose uncertainties and mitigate the effect of outliers. Most of these concepts are considered in classical approaches, e.g. from the Bayesian and Kalman filtering literature, which however are not as powerful as Deep Learning in handling visual data. As a consequence, the development of hybrid architectures that combine the best features from both worlds is particularly appealing in a robotic setting. Motivated by these considerations, in this Thesis, I aimed at devising hybrid architectures for object perception, focusing on the task of object pose and velocity tracking. The proposed architectures use Kalman filtering supported by state-of-the-art Deep Neural Networks to track the 6D pose and velocity of objects from images. The devised solutions exhibit state-of-the-art performance, increased modularity and do not require training to implement the actual tracking behaviors. Furthermore, they can track even fast object motions despite the possible non-negligible inference times of the adopted neural networks. Also, by relying on data-driven Kalman filtering, I explored a paradigm that enables to track the state of systems that cannot be easily modeled analytically. Specifically, I used this approach to learn the measurement model of soft 3D tactile sensors and address the problem of tracking the sliding motion of hand-held objects

Evaluating the differences and accuracies between GNSS applications using PPP

Global Navigation Satellite Systems (GNSS) are satellite systems with global coverage. There are currently several GNSS systems in operation today including the United States NAVSTAR Global Positioning System, Russian GLONASS, Chinese Beidou and the European Union’s Galileo system. The Galileo and Beidou systems are currently undergoing upgrading in order to achieve more sustainable and comprehensive worldwide exposure, ultimately providing users with a broader option of systems and wider more reliable coverage. In recent years, in addition to the GPS constellation, the ability to utilise extra satellites made available through the GLONASS and Beidou systems has enhanced the capabilities and possible applications of the precise point positioning (PPP) method. Precise Point Positioning has been used for the last decade as a cost-effective alternative to conventional DGPS-Differential GPS with an estimated precision adequate for many applications. PPP requires handling different types of errors using proper models. PPP precision varies with the use of observations from different satellite systems (GPS, GLONASS and mixed GPS/GLONASS/Beidou) and the duration of observations. However, the fundamental differences between GPS, GLONASS, Beidou and Galileo and the lack of a fully tested global tracking network of multi-Global Navigation Satellite Systems necessitate the evaluation of their combined use. More studies are required in order to confirm the reliability and accuracy of the results obtained by the various methods of PPP. This is outside the scope of this paper. This research paper will evaluate and analyse the accuracy and reliability between different GNSS systems using the Precise Point Positioning technique with emphasis on the function and performance of single systems compared with combined GNSS systems. A methodology was designed to ensure accurate and reliable results have been achieved. Solutions generated from identical data will be compared for bias, accuracy and reliability between single standalone GPS and combined GNSS systems. This study focused on the performance of these systems over a twenty four hour observation period, decimated into 1, 2, 6, 12 and 24 hours. The study found that the reliability and performance of GNSS systems over standalone GPS was insignificant over a twenty four hour period. In fact, where satellite availability and constellation are at a premium, standalone GPS systems can produce equivalent quality results compared with combined GNSS. Having said this, the combined GNSS systems achieved quicker convergence times than standalone systems. With limited access and availability to resources, in particular GNSS receivers, the results can be seen as preliminary testing enhancing the knowledge of GNSS users. Nonetheless, this dissertation covers a wide range of topics and field testing providing relevant reliable data on the accuracy, precision and performance of both standalone and combined Global Navigation Satellite Systems

Implementation of a acceleration estimator based compensation scheme to increase load data accuracy for a robotic testing system for CPR-manikins

Laerdal Medical is a producer of Cardiopulmonary Resuscitation (CPR) training manikins, all of which undergo rigorous endurance and accuracy testing. This work proposes an acceleration estimator based compensation scheme for a industrial robot manipulator product testing system with the intention of increasing load data accuracy for the purpose of product review and calibration. As part of the compensation scheme four different acceleration estimators are implemented and compared. Results indicate that the compensation scheme increases the load data accuracy by 1.5 - 6 % of the reference value depending on compression depth and spring rate. However the accuracy goal of 0.4 [kg] is not reached. The work has also uncovered the presence of position error in the robot. Thus, further improvement to the compensation scheme and positional error compensation is required