Geo-visual analytics for urban design in the context of future Internet

The internet, where much of the information has reference to location, together with the latest generation of geographical web services, represent a very large information space that can be used for planning and design. The wealth of information accessible, which requires new forms of interaction and management of the data available, has brought in recent year to the growth of the domain of visual analytics. In addition, the availability of 3D geobrowsers provides the technological means for interactive 3D environments which can be used to access large-scale geographical information. This technological scenario is paving the way to 3D webbased, geo-visual analytics tools for land planning and urban design tools. This paper illustrates the results of a research effort which has brought to the development of an interactive geo-visual analytics platform for land planning and urban design which makes use of procedural modelling algorithms

Self-Supervised Shape and Appearance Modeling via Neural Differentiable Graphics

Inferring 3D shape and appearance from natural images is a fundamental challenge in computer vision. Despite recent progress using deep learning methods, a key limitation is the availability of annotated training data, as acquisition is often very challenging and expensive, especially at a large scale. This thesis proposes to incorporate physical priors into neural networks that allow for self-supervised learning. As a result, easy-to-access unlabeled data can be used for model training. In particular, novel algorithms in the context of 3D reconstruction and texture/material synthesis are introduced, where only image data is available as supervisory signal. First, a method that learns to reason about 3D shape and appearance solely from unstructured 2D images, achieved via differentiable rendering in an adversarial fashion, is proposed. As shown next, learning from videos significantly improves 3D reconstruction quality. To this end, a novel ray-conditioned warp embedding is proposed that aggregates pixel-wise features from multiple source images. Addressing the challenging task of disentangling shape and appearance, first a method that enables 3D texture synthesis independent of shape or resolution is presented. For this purpose, 3D noise fields of different scales are transformed into stationary textures. The method is able to produce 3D textures, despite only requiring 2D textures for training. Lastly, the surface characteristics of textures under different illumination conditions are modeled in the form of material parameters. Therefore, a self-supervised approach is proposed that has no access to material parameters but only flash images. Similar to the previous method, random noise fields are reshaped to material parameters, which are conditioned to replicate the visual appearance of the input under matching light

Coastal On-line Assessment and Synthesis Tool 2.0

COAST (Coastal On-line Assessment and Synthesis Tool) is a 3D, open-source Earth data browser developed by leveraging and enhancing previous NASA open-source tools. These tools use satellite imagery and elevation data in a way that allows any user to zoom from orbit view down into any place on Earth, and enables the user to experience Earth terrain in a visually rich 3D view. The benefits associated with taking advantage of an open-source geo-browser are that it is free, extensible, and offers a worldwide developer community that is available to provide additional development and improvement potential. What makes COAST unique is that it simplifies the process of locating and accessing data sources, and allows a user to combine them into a multi-layered and/or multi-temporal visual analytical look into possible data interrelationships and coeffectors for coastal environment phenomenology. COAST provides users with new data visual analytic capabilities. COAST has been upgraded to maximize use of open-source data access, viewing, and data manipulation software tools. The COAST 2.0 toolset has been developed to increase access to a larger realm of the most commonly implemented data formats used by the coastal science community. New and enhanced functionalities that upgrade COAST to COAST 2.0 include the development of the Temporal Visualization Tool (TVT) plug-in, the Recursive Online Remote Data-Data Mapper (RECORD-DM) utility, the Import Data Tool (IDT), and the Add Points Tool (APT). With these improvements, users can integrate their own data with other data sources, and visualize the resulting layers of different data types (such as spatial and spectral, for simultaneous visual analysis), and visualize temporal changes in areas of interest

Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks

It is common to implicitly assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is itself a major challenge. We address the problem of learning to look around: if a visual agent has the ability to voluntarily acquire new views to observe its environment, how can it learn efficient exploratory behaviors to acquire informative observations? We propose a reinforcement learning solution, where the agent is rewarded for actions that reduce its uncertainty about the unobserved portions of its environment. Based on this principle, we develop a recurrent neural network-based approach to perform active completion of panoramic natural scenes and 3D object shapes. Crucially, the learned policies are not tied to any recognition task nor to the particular semantic content seen during training. As a result, 1) the learned "look around" behavior is relevant even for new tasks in unseen environments, and 2) training data acquisition involves no manual labeling. Through tests in diverse settings, we demonstrate that our approach learns useful generic policies that transfer to new unseen tasks and environments. Completion episodes are shown at https://goo.gl/BgWX3W

Photogrammetric 3D Scanning of Physical Objects: Tools and Workflow

Ease of access to and low cost of hardware and software for 3D scanning have made 3D technologies increasingly popular in recent research. One of the possible 3D scanning approaches is photogrammetry which relies on using a data set consisting of photographs of the same physical object. In this paper are evaluated different 3D models generated from the same input data set by specialised software packages for photogrammetry. The main attributes of the 3D models are examined in comparative analyses and their differences highlighted. Furthermore, visual qualitative inspections are performed on the models and the results are compared

T4DT: Tensorizing Time for Learning Temporal 3D Visual Data

Unlike 2D raster images, there is no single dominant representation for 3D visual data processing. Different formats like point clouds, meshes, or implicit functions each have their strengths and weaknesses. Still, grid representations such as signed distance functions have attractive properties also in 3D. In particular, they offer constant-time random access and are eminently suitable for modern machine learning. Unfortunately, the storage size of a grid grows exponentially with its dimension. Hence they often exceed memory limits even at moderate resolution. This work explores various low-rank tensor formats, including the Tucker, tensor train, and quantics tensor train decompositions, to compress time-varying 3D data. Our method iteratively computes, voxelizes, and compresses each frame's truncated signed distance function and applies tensor rank truncation to condense all frames into a single, compressed tensor that represents the entire 4D scene. We show that low-rank tensor compression is extremely compact to store and query time-varying signed distance functions. It significantly reduces the memory footprint of 4D scenes while surprisingly preserving their geometric quality. Unlike existing iterative learning-based approaches like DeepSDF and NeRF, our method uses a closed-form algorithm with theoretical guarantees

BIM and Game Engine Integration for Operational Data Monitoring in Buildings

Building Information Modelling (BIM), as a new approach to the digital representation of the whole building lifecycle, including design, construction, building operation and maintenance, increases the efficiency and productivity of the architecture, engineering, and construction (AEC) industries. Because of complicated and comprehensive building information, BIM by itself is not able to provide an interactive visual environment for stakeholders and mobile access to the building information model is limited too. This paper aims to integrate BIM and 3D game engines to provide a real-time monitoring, mobile and interactive model. We’ve developed this building model as a serious game, capable of running on both Windows and IOS platforms. Players go through a virtual building, check enclosure materials, MEP systems, and real-time operating data in the game. A case study has been developed to show the benefits of integrating BIM and 3D game engines for modern building management

Contributions to virtual reality

153 p.The thesis contributes in three Virtual Reality areas: ¿ Visual perception: a calibration algorithm is proposed to estimate stereo projection parameters in head-mounted displays, so that correct shapes and distances can be perceived, and calibration and control procedures are proposed to obtain desired accommodation stimuli at different virtual distances.¿ Immersive scenarios: the thesis analyzes several use cases demanding varying degrees of immersion and special, innovative visualization solutions are proposed to fulfil their requirements. Contributions focus on machinery simulators, weather radar volumetric visualization and manual arc welding simulation.¿ Ubiquitous visualization: contributions are presented to scenarios where users access interactive 3D applications remotely. The thesis follows the evolution of Web3D standards and technologies to propose original visualization solutions for volume rendering of weather radar data, e-learning on energy efficiency, virtual e-commerce and visual product configurators