Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

Automatic High-Fidelity 3D Road Network Modeling

Many computer applications such as racing games and driving simulations frequently make use of 3D high-fidelity road network models for a variety of purposes. However, there are very few existing methods for automatic generation of 3D realistic road networks, especially for those in the real world. On the other hand, vast road network GIS data have been collected in the past and used by a wide range of applications, such as navigation and evaluation. A method that can automatically produce 3D high-fidelity road network models from 2D real road GIS data will significantly reduce both the labor and time needed to generate these models, and greatly benefit numerous applications involving road networks. Based on a set of selected civil engineering rules for road design, this dissertation research addresses this problem with a novel approach which transforms existing road GIS data that contain only 2D road centerline information into 3D road network models. The proposed method consists of several components, mainly including road GIS data preprocessing, 3D centerline modeling and 3D geometry modeling. During road data preprocessing, topology of the road network is extracted from raw road data as a graph composed of road nodes and road links; road link information is simplified and classified. In the 3D centerline modeling part, the missing height information of the road centerline is inferred based on 2D road GIS data, intersections are extracted from road nodes and the whole road network is represented as road intersections and road segments in parametric forms. Finally, the 3D road centerline models are converted into various 3D road geometry models consisting of triangles and textures in the 3D geometry modeling phase. With this approach, basic road elements such as road segments, road intersections and traffic interchanges are generated automatically to compose sophisticated road networks. Results show that this approach provides a rapid and efficient 3D road modeling method for applications that have stringent requirements on high-fidelity road models

Modular Human-in-the-loop Design Framework Based on Human Factors

Human-in-the-loop design framework introduced in this dissertation utilizes Digital Human Modeling (DHM) to incorporate Human Factors Engineering (HFE) design principles early in design process. It embodies scientific methods (e.g., mathematics) and artistic approaches (e.g., visualization) to assess human well-being and overall system performance. This framework focuses not only on ergonomics assessments but also actual design process including, but not limited to, concept development, structural integrity and digital prototyping. It addresses to three major limitations found in HFE literature and practices

Augmenting low-fidelity flight simulation training devices via amplified head rotations

Due to economic and operational constraints, there is an increasing demand from aviation operators and training manufacturers to extract maximum training usage from the lower fidelity suite of flight simulators. It is possible to augment low-fidelity flight simulators to achieve equivalent performance compared to high-fidelity setups but at reduced cost and greater mobility. In particular for visual manoeuvres, the virtual reality technique of head-tracking amplification for virtual view control enables full field-of-regard access even with limited field-of-view displays. This research quantified the effects of this technique on piloting performance, workload and simulator sickness by applying it to a fixed-base, low-fidelity, low-cost flight simulator. In two separate simulator trials, participants had to land a simulated aircraft from a visual traffic circuit pattern whilst scanning for airborne traffic. Initially, a single augmented display was compared to the common triple display setup in front of the pilot. Starting from the base leg, pilots exhibited tighter turns closer to the desired ground track and were more actively conducting visual scans using the augmented display. This was followed up by a second experiment to quantify the scalability of augmentation towards larger displays and field of views. Task complexity was increased by starting the traffic pattern from the downwind leg. Triple displays in front of the pilot yielded the best compromise delivering flight performance and traffic detection scores just below the triple projectors but without an increase in track deviations and the pilots were also less prone to simulator sickness symptoms. This research demonstrated that head augmentation yields clear benefits of quick user adaptation, low-cost, ease of systems integration, together with the capability to negate the impact of display sizes yet without incurring significant penalties in workload and incurring simulator sickness. The impact of this research is that it facilitates future flight training solutions using this augmentation technique to meet budgetary and mobility requirements. This enables deployment of simulators in large numbers to deliver expanded mission rehearsal previously unattainable within this class of low-fidelity simulators, and with no restrictions for transfer to other training media

All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes

We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (www.urbansyn.org).Comment: The UrbanSyn Dataset is available in http://urbansyn.org

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin