Cuboid-maps for indoor illumination modeling and augmented reality rendering

This thesis proposes a novel approach for indoor scene illumination modeling and augmented reality rendering. Our key observation is that an indoor scene is well represented by a set of rectangular spaces, where important illuminants reside on their boundary faces, such as a window on a wall or a ceiling light. Given a perspective image or a panorama and detected rectangular spaces as inputs, we estimate their cuboid shapes, and infer illumination components for each face of the cuboids by a simple convolutional neural architecture. The process turns an image into a set of cuboid environment maps, each of which is a simple extension of a traditional cube-map. For augmented reality rendering, we simply take a linear combination of inferred environment maps and an input image, producing surprisingly realistic illumination effects. This approach is simple and efficient, avoids flickering, and achieves quantitatively more accurate and qualitatively more realistic effects than competing substantially more complicated systems

Data-driven depth and 3D architectural layout estimation of an interior environment from monocular panoramic input

Recent years have seen significant interest in the automatic 3D reconstruction of indoor scenes, leading to a distinct and very-active sub-field within 3D reconstruction. The main objective is to convert rapidly measured data representing real-world indoor environments into models encompassing geometric, structural, and visual abstractions. This thesis focuses on the particular subject of extracting geometric information from single panoramic images, using either visual data alone or sparse registered depth information. The appeal of this setup lies in the efficiency and cost-effectiveness of data acquisition using 360o images. The challenge, however, is that creating a comprehensive model from mostly visual input is extremely difficult, due to noise, missing data, and clutter. My research has concentrated on leveraging prior information, in the form of architectural and data-driven priors derived from large annotated datasets, to develop end-to-end deep learning solutions for specific tasks in the structured reconstruction pipeline. My first contribution consists in a deep neural network architecture for estimating a depth map from a single monocular indoor panorama, operating directly on the equirectangular projection. Leveraging the characteristics of indoor 360-degree images and recognizing the impact of gravity on indoor scene design, the network efficiently encodes the scene into vertical spherical slices. By exploiting long- and short- term relationships among these slices, it recovers an equirectangular depth map directly from the corresponding RGB image. My second contribution generalizes the approach to handle multimodal input, also covering the situation in which the equirectangular input image is paired with a sparse depth map, as provided from common capture setups. Depth is inferred using an efficient single-branch network with a dynamic gating system, processing both dense visual data and sparse geometric data. Additionally, a new augmentation strategy enhances the model's robustness to various types of sparsity, including those from structured light sensors and LiDAR setups. While the first two contributions focus on per-pixel geometric information, my third contribution addresses the recovery of the 3D shape of permanent room surfaces from a single panoramic image. Unlike previous methods, this approach tackles the problem in 3D, expanding the reconstruction space. It employs a graph convolutional network to directly infer the room structure as a 3D mesh, deforming a graph- encoded tessellated sphere mapped to the spherical panorama. Gravity- aligned features are actively incorporated using a projection layer with multi-head self-attention, and specialized losses guide plausible solutions in the presence of clutter and occlusions. The benchmarks on publicly available data show that all three methods provided significant improvements over the state-of-the-art

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. While highly innovative and effective, these products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide a radical alternative to such data-intensive procedures by presentingWalk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both trajectories simulated from scanned models of interiors and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm that our technique is viable and allows recovering reliable floor plans from minimal walk trajectory data

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. While highly innovative and effective, these products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide a radical alternative to such data-intensive procedures by presenting Walk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both trajectories simulated from scanned models of interiors and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm that our technique is viable and allows recovering reliable floor plans from minimal walk trajectory data

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. These products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide an alternative to such data-intensive procedures by presenting Walk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both simulated and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm the feasibility of our approach.Comment: To be published in Computer Graphics Forum (Proc. Eurographics 2021

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Senseable Spaces: from a theoretical perspective to the application in augmented environments

openGrazie all’ enorme diffusione di dispositivi senzienti nella vita di tutti i giorni, nell’ ultimo decennio abbiamo assistito ad un cambio definitivo nel modo in cui gli utenti interagiscono con lo spazio circostante. Viene coniato il termine Spazio Sensibile, per descrivere quegli spazi in grado di fornire servizi contestuali agli utenti, misurando e analizzando le dinamiche che in esso avvengono, e di reagire conseguentemente a questo continuo flusso di dati bidirezionale. La ricerca è stata condotta abbracciando diversi domini di applicazione, le cui singole esigenze hanno reso necessario testare il concetto di Spazi Sensibili in diverse declinazioni, mantenendo al centro della ricerca l’utente, con la duplice accezione di end-user e manager. Molteplici sono i contributi rispetto allo stato dell’ arte. Il concetto di Spazio Sensibile è stato calato nel settore dei Beni Culturali, degli Spazi Pubblici, delle Geosciences e del Retail. I casi studio nei musei e nella archeologia dimostrano come l’ utilizzo della Realtà Aumentata possa essere sfruttata di fronte a un dipinto o in outdoor per la visualizzazione di modelli complessi, In ambito urbano, il monitoraggio di dati generati dagli utenti ha consentito di capire le dinamiche di un evento di massa, durante il quale le stesse persone fruivano di servizi contestuali. Una innovativa applicazione di Realtà Aumentata è stata come servizio per facilitare l’ ispezione di fasce tampone lungo i fiumi, standardizzando flussi di dati e modelli provenienti da un Sistema Informativo Territoriale. Infine, un robusto sistema di indoor localization è stato istallato in ambiente retail, per scopi classificazione dei percorsi e per determinare le potenzialità di un punto vendita. La tesi è inoltre una dimostrazione di come Space Sensing e Geomatica siano discipline complementari: la geomatica consente di acquisire e misurare dati geo spaziali e spazio temporali a diversa scala, lo Space Sensing utilizza questi dati per fornire servizi all’ utente precisi e contestuali.Given the tremendous growth of ubiquitous services in our daily lives, during the last few decades we have witnessed a definitive change in the way users' experience their surroundings. At the current state of art, devices are able to sense the environment and users’ location, enabling them to experience improved digital services, creating synergistic loop between the use of the technology, and the use of the space itself. We coined the term Senseable Space, to define the kinds of spaces able to provide users with contextual services, to measure and analyse their dynamics and to react accordingly, in a seamless exchange of information. Following the paradigm of Senseable Spaces as the main thread, we selected a set of experiences carried out in different fields; central to this investigation there is of course the user, placed in the dual roles of end-user and manager. The main contribution of this thesis lies in the definition of this new paradigm, realized in the following domains: Cultural Heritage, Public Open Spaces, Geosciences and Retail. For the Cultural Heritage panorama, different pilot projects have been constructed from creating museum based installations to developing mobile applications for archaeological settings. Dealing with urban areas, app-based services are designed to facilitate the route finding in a urban park and to provide contextual information in a city festival. We also outlined a novel application to facilitate the on-site inspection by risk managers thanks to the use of Augmented Reality services. Finally, a robust indoor localization system has been developed, designed to ease customer profiling in the retail sector. The thesis also demonstrates how Space Sensing and Geomatics are complementary to one another, given the assumption that the branches of Geomatics cover all the different scales of data collection, whilst Space Sensing gives one the possibility to provide the services at the correct location, at the correct time.INGEGNERIA DELL'INFORMAZIONEembargoed_20181001Pierdicca, RobertoPierdicca, Robert

Senseable Spaces: from a theoretical perspective to the application in augmented environments

Grazie all’ enorme diffusione di dispositivi senzienti nella vita di tutti i giorni, nell’ ultimo decennio abbiamo assistito ad un cambio definitivo nel modo in cui gli utenti interagiscono con lo spazio circostante. Viene coniato il termine Spazio Sensibile, per descrivere quegli spazi in grado di fornire servizi contestuali agli utenti, misurando e analizzando le dinamiche che in esso avvengono, e di reagire conseguentemente a questo continuo flusso di dati bidirezionale. La ricerca è stata condotta abbracciando diversi domini di applicazione, le cui singole esigenze hanno reso necessario testare il concetto di Spazi Sensibili in diverse declinazioni, mantenendo al centro della ricerca l’utente, con la duplice accezione di end-user e manager. Molteplici sono i contributi rispetto allo stato dell’ arte. Il concetto di Spazio Sensibile è stato calato nel settore dei Beni Culturali, degli Spazi Pubblici, delle Geosciences e del Retail. I casi studio nei musei e nella archeologia dimostrano come l’ utilizzo della Realtà Aumentata possa essere sfruttata di fronte a un dipinto o in outdoor per la visualizzazione di modelli complessi, In ambito urbano, il monitoraggio di dati generati dagli utenti ha consentito di capire le dinamiche di un evento di massa, durante il quale le stesse persone fruivano di servizi contestuali. Una innovativa applicazione di Realtà Aumentata è stata come servizio per facilitare l’ ispezione di fasce tampone lungo i fiumi, standardizzando flussi di dati e modelli provenienti da un Sistema Informativo Territoriale. Infine, un robusto sistema di indoor localization è stato istallato in ambiente retail, per scopi classificazione dei percorsi e per determinare le potenzialità di un punto vendita. La tesi è inoltre una dimostrazione di come Space Sensing e Geomatica siano discipline complementari: la geomatica consente di acquisire e misurare dati geo spaziali e spazio temporali a diversa scala, lo Space Sensing utilizza questi dati per fornire servizi all’ utente precisi e contestuali.Given the tremendous growth of ubiquitous services in our daily lives, during the last few decades we have witnessed a definitive change in the way users' experience their surroundings. At the current state of art, devices are able to sense the environment and users’ location, enabling them to experience improved digital services, creating synergistic loop between the use of the technology, and the use of the space itself. We coined the term Senseable Space, to define the kinds of spaces able to provide users with contextual services, to measure and analyse their dynamics and to react accordingly, in a seamless exchange of information. Following the paradigm of Senseable Spaces as the main thread, we selected a set of experiences carried out in different fields; central to this investigation there is of course the user, placed in the dual roles of end-user and manager. The main contribution of this thesis lies in the definition of this new paradigm, realized in the following domains: Cultural Heritage, Public Open Spaces, Geosciences and Retail. For the Cultural Heritage panorama, different pilot projects have been constructed from creating museum based installations to developing mobile applications for archaeological settings. Dealing with urban areas, app-based services are designed to facilitate the route finding in a urban park and to provide contextual information in a city festival. We also outlined a novel application to facilitate the on-site inspection by risk managers thanks to the use of Augmented Reality services. Finally, a robust indoor localization system has been developed, designed to ease customer profiling in the retail sector. The thesis also demonstrates how Space Sensing and Geomatics are complementary to one another, given the assumption that the branches of Geomatics cover all the different scales of data collection, whilst Space Sensing gives one the possibility to provide the services at the correct location, at the correct time

Automatic Reconstruction of Textured 3D Models

Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models

Visual Perception For Robotic Spatial Understanding

Humans understand the world through vision without much effort. We perceive the structure, objects, and people in the environment and pay little direct attention to most of it, until it becomes useful. Intelligent systems, especially mobile robots, have no such biologically engineered vision mechanism to take for granted. In contrast, we must devise algorithmic methods of taking raw sensor data and converting it to something useful very quickly. Vision is such a necessary part of building a robot or any intelligent system that is meant to interact with the world that it is somewhat surprising we don\u27t have off-the-shelf libraries for this capability. Why is this? The simple answer is that the problem is extremely difficult. There has been progress, but the current state of the art is impressive and depressing at the same time. We now have neural networks that can recognize many objects in 2D images, in some cases performing better than a human. Some algorithms can also provide bounding boxes or pixel-level masks to localize the object. We have visual odometry and mapping algorithms that can build reasonably detailed maps over long distances with the right hardware and conditions. On the other hand, we have robots with many sensors and no efficient way to compute their relative extrinsic poses for integrating the data in a single frame. The same networks that produce good object segmentations and labels in a controlled benchmark still miss obvious objects in the real world and have no mechanism for learning on the fly while the robot is exploring. Finally, while we can detect pose for very specific objects, we don\u27t yet have a mechanism that detects pose that generalizes well over categories or that can describe new objects efficiently. We contribute algorithms in four of the areas mentioned above. First, we describe a practical and effective system for calibrating many sensors on a robot with up to 3 different modalities. Second, we present our approach to visual odometry and mapping that exploits the unique capabilities of RGB-D sensors to efficiently build detailed representations of an environment. Third, we describe a 3-D over-segmentation technique that utilizes the models and ego-motion output in the previous step to generate temporally consistent segmentations with camera motion. Finally, we develop a synthesized dataset of chair objects with part labels and investigate the influence of parts on RGB-D based object pose recognition using a novel network architecture we call PartNet