scholarly journals Automated Data Annotation for 6-DoF AI-Based Navigation Algorithm Development

2021 ◽  
Vol 7 (11) ◽  
pp. 236
Author(s):  
Javier Gibran Apud Baca ◽  
Thomas Jantos ◽  
Mario Theuermann ◽  
Mohamed Amin Hamdad ◽  
Jan Steinbrener ◽  
...  
Keyword(s):  
Ground Truth ◽  
Autonomous Driving ◽  
3D Models ◽  
Unmanned Aircraft ◽  
Volume Estimation ◽  
Data Annotation ◽  
Navigation Algorithm ◽  
Novel Approach ◽  
Six Degree Of Freedom

Accurately estimating the six degree of freedom (6-DoF) pose of objects in images is essential for a variety of applications such as robotics, autonomous driving, and autonomous, AI, and vision-based navigation for unmanned aircraft systems (UAS). Developing such algorithms requires large datasets; however, generating those is tedious as it requires annotating the 6-DoF relative pose of each object of interest present in the image w.r.t. to the camera. Therefore, this work presents a novel approach that automates the data acquisition and annotation process and thus minimizes the annotation effort to the duration of the recording. To maximize the quality of the resulting annotations, we employ an optimization-based approach for determining the extrinsic calibration parameters of the camera. Our approach can handle multiple objects in the scene, automatically providing ground-truth labeling for each object and taking into account occlusion effects between different objects. Moreover, our approach can not only be used to generate data for 6-DoF pose estimation and corresponding 3D-models but can be also extended to automatic dataset generation for object detection, instance segmentation, or volume estimation for any kind of object.

Enhancing Model Reuse Through 3D Annotations: A Theoretical Proposal for an Annotation-Centered Design Intent and Design Rationale Communication

2013 ◽  
Author(s):  
Jorge Dorribo-Camba ◽  
Gerardo Alducin-Quintero ◽  
Pascual Perona ◽  
Manuel Contero
Keyword(s):  
3D Models ◽  
Design Rationale ◽  
Design Intent ◽  
Data Annotation ◽  
3D Cad ◽  
Cad Models ◽  
The Impact ◽  
Theoretical Proposal

The long term goals of this research are to study the effectiveness of CAD 3D annotation techniques to support the explicit communication of design intent and rationale, and to analyze the impact of the annotations in the alteration and reutilization of 3D models in a product design context. Towards these goals, we are initially examining the formal annotation practices defined by model-based standards such as ASME Y14.41-2012 and ISO 16792:2006, and their implementation in current CAD systems. This paper presents a prototype implementation of a module to automatically extract textual information from annotated 3D CAD models. Automated extraction of data annotation can be used to analyze both the content and the quality of the annotations with the purpose of determining what makes annotations effective and ultimately communicating design intent. The architecture of a system designed to manage and manipulate this information is also described and analyzed.

scholarly journals Quality assessment for 3D reconstruction of building interiors

2020 ◽  
Vol 2020 (17) ◽  
pp. 36-1-36-7
Author(s):  
Umamaheswaran RAMAN KUMAR ◽  
Inge COUDRON ◽  
Steven PUTTEMANS ◽  
Patrick VANDEWALLE
Keyword(s):  
3D Reconstruction ◽  
Real World ◽  
State Of The Art ◽  
Ground Truth ◽  
3D Models ◽  
Indoor Environments ◽  
Ground Truth Data ◽  
Complex Design ◽  
Reconstructed Model

Applications ranging from simple visualization to complex design require 3D models of indoor environments. This has given rise to advancements in the field of automated reconstruction of such models. In this paper, we review several state-of-the-art metrics proposed for geometric comparison of 3D models of building interiors. We evaluate their performance on a real-world dataset and propose one tailored metric which can be used to assess the quality of the reconstructed model. In addition, the proposed metric can also be easily visualized to highlight the regions or structures where the reconstruction failed. To demonstrate the versatility of the proposed metric we conducted experiments on various interior models by comparison with ground truth data created by expert Blender artists. The results of the experiments were then used to improve the reconstruction pipeline.

scholarly journals Creating and Exploring Semantic Annotation for Behaviour Analysis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2778 ◽  
Author(s):  
Kristina Yordanova ◽  
Frank Krüger
Keyword(s):  
Interrater Reliability ◽  
Recognition Performance ◽  
Semantic Annotation ◽  
Ground Truth ◽  
Training Data ◽  
Grand Challenge ◽  
Behaviour Analysis ◽  
Knowledge Based ◽  
Novel Approach

Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen’s κ of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset.

scholarly journals THERMAL TEXTURE GENERATION AND 3D MODEL RECONSTRUCTION USING SFM AND GAN

2018 ◽  
Vol XLII-2 ◽  
pp. 519-524 ◽  
Author(s):  
V. V. Kniaz ◽  
V. A. Mizginov
Keyword(s):  
Thermal Emission ◽  
Ground Truth ◽  
Autonomous Driving ◽  
3D Models ◽  
Automatic Generation ◽  
Generative Adversarial Networks ◽  
Visible Range ◽  
Geometrical Shape ◽  
Infrared Imagery ◽  
Thermal Range

Realistic 3D models with textures representing thermal emission of the object are widely used in such fields as dynamic scene analysis, autonomous driving, and video surveillance. Structure from Motion (SfM) methods provide a robust approach for the generation of textured 3D models in the visible range. Still, automatic generation of 3D models from the infrared imagery is challenging due to an absence of the feature points and low sensor resolution. Recent advances in Generative Adversarial Networks (GAN) have proved that they can perform complex image-to-image transformations such as a transformation of day to night and generation of imagery in a different spectral range. In this paper, we propose a novel method for generation of realistic 3D models with thermal textures using the SfM pipeline and GAN. The proposed method uses visible range images as an input. The images are processed in two ways. Firstly, they are used for point matching and dense point cloud generation. Secondly, the images are fed into a GAN that performs the transformation from the visible range to the thermal range. We evaluate the proposed method using real infrared imagery captured with a FLIR ONE PRO camera. We generated a dataset with 2000 pairs of real images captured in thermal and visible range. The dataset is used to train the GAN network and to generate 3D models using SfM. The evaluation of the generated 3D models and infrared textures proved that they are similar to the ground truth model in both thermal emissivity and geometrical shape.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

scholarly journals One Song, Many Voices: Dementia and The Power of Music

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 775-775
Author(s):  
Debra Sheets ◽  
Stuart MacDonald ◽  
Andre Smith
Keyword(s):  
High School Students ◽  
Social Inclusion ◽  
Positive Impact ◽  
Social Connections ◽  
Social Connection ◽  
School Students ◽  
Choral Singing ◽  
Novel Approach ◽  
Care Partners

Abstract Choral singing is a novel approach to reduce dementia stigma and social isolation while offering participants a sense of purpose, joy and social connection. The pervasiveness of stigma surrounding dementia remains one of the biggest barriers to living life with dignity following a diagnosis (Alzheimer Society of Canada, 2018). This paper examines how a social inclusion model of dementia care involving an intergenerational choir for people living with dementia, their care partners and high school students can reduce stigma and foster social connections. Multiple methodologies are used to investigate the effects of choir participation on cognition, stress levels, social connections, stigma, and quality of life. Results demonstrate the positive impact of choir participation and indicate that this socially inclusive intervention offers an effective, non-pharmacological alternative for older adults living with dementia in the community. Discussion focuses on the importance of instituting meaningful and engaging dementia-friendly activities at the community level.

scholarly journals RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1299
Author(s):  
Honglin Yuan ◽  
Tim Hoogenkamp ◽  
Remco C. Veltkamp
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
3D Models ◽  
Depth Image ◽  
Great Success ◽  
Estimation Algorithms ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Image Pairs ◽  
Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

scholarly journals A Novel Approach for Comparison of Reception Performance in a Technique Test and Small-Sided Games

Sports ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 66
Author(s):  
Arne Sørensen ◽  
Vidar Sørensen ◽  
Terje Dalen
Keyword(s):  
Correlation Coefficient ◽  
Rank Order ◽  
Technical Skills ◽  
Quality Score ◽  
Soccer Players ◽  
Moderate Correlation ◽  
Technical Aspects ◽  
Youth Soccer ◽  
Novel Approach

The purpose of this study was to evaluate the correlation between soccer players’ performance of receptions of passes in tests of both isolated technical skills and more match-realistic situations in small-sided games (SSGs). In addition, this study investigated whether the involvement in SSGs (number of receptions) correlated with the quality of receptions in the respective SSGs. The participants were 13 male outfield youth soccer players from teams in the first division of the regional U18 league. The quality of receptions was scored by educated coaches according to set criteria of performance. Statistical analyses of correlations were determined using Spearman’s rank-order correlation coefficient (rs). The main results were (1) a significant correlation in the quality of ball reception between 4vs1 SSGs and 5vs5 SSGs (rs = −0.61, p < 0.01) and (2) a trend towards moderate correlation between the quality of ball reception using a ball projection machine and 5vs5 SSGs (rs = −0.48, p = 0.10). (3) A significant correlation was found between the number of receptions in 5vs5 SSGs and the quality score of receptions in 5vs5 SSGs (rs = −0.70, p < 0.01). The trend towards moderate correlations between 5vs5 SSGs and the isolated technical reception test could imply the importance of training in the technical aspects of ball reception. Moreover, it seems as though the players with the best reception performance are the players who are most involved in SSGs, that is, having the most receptions.

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