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We present MoReLab, a tool for user-assisted 3D reconstruction. This reconstruction requires an understanding of the shapes of the desired objects. Our experiments demonstrate that existing Structure from Motion (SfM) software packages fail to estimate accurate 3D models in low-quality videos due to several issues such as low resolution, featureless surfaces, low lighting, etc. In such scenarios, which are common for industrial utility companies, user assistance becomes necessary to create reliable 3D models. In our system, the user first needs to add features and correspondences manually on multiple video frames. Then, classic camera calibration and bundle adjustment are applied. At this point, MoReLab provides several primitive shape tools such as rectangles, cylinders, curved cylinders, etc., to model different parts of the scene and export 3D meshes. These shapes are essential for modeling industrial equipment whose videos are typically captured by utility companies with old video cameras (low resolution, compression artifacts, etc.) and in disadvantageous lighting conditions (low lighting, torchlight attached to the video camera, etc.). We evaluate our tool on real industrial case scenarios and compare it against existing approaches. Visual comparisons and quantitative results show that MoReLab achieves superior results with regard to other user-interactive 3D modeling tools.

The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automatized by modern CNNs. This paper extends our previous work presented at the 3DUW’19 conference, outlining the workflow for the automated annotation of imagery from the first step of dataset preparation, to the last step of prediction reassembly. In particular, we propose an ecologically inspired strategy for an efficient dataset partition, an over-sampling methodology targeted on ortho-imagery, and a score fusion strategy. We also investigate the use of different loss functions in the optimization of a Deeplab V3+ model, to mitigate the class-imbalance problem and improve prediction accuracy on coral instance boundaries. The experimental results demonstrate the effectiveness of the ecologically inspired split in improving model performance, and quantify the advantages and limitations of the proposed over-sampling strategy. The extensive comparison of the loss functions gives numerous insights on the segmentation task; the Focal Tversky, typically used in the context of medical imaging (but not in remote sensing), results in the most convenient choice. By improving the accuracy of automated ortho image processing, the results presented here promise to meet the fundamental challenge of increasing the spatial and temporal scale of coral reef research, allowing researchers greater predictive ability to better manage coral reef resilience in the context of a changing environment.

Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts.

Marine animal forests are benthic communities dominated by sessile suspension feeders (such as sponges, corals, and bivalves) able to generate three-dimensional (3D) frameworks with high structural complexity. The biodiversity and functioning of marine animal forests are strictly related to their 3D complexity. The present paper aims at providing new perspectives in underwater optical surveys. Starting from the current gaps in data collection and analysis that critically limit the study and conservation of marine animal forests, we discuss the main technological and methodological needs for the investigation of their 3D structural complexity at different spatial and temporal scales. Despite recent technological advances, it seems that several issues in data acquisition and processing need to be solved, to properly map the different benthic habitats in which marine animal forests are present, their health status and to measure structural complexity. Proper precision and accuracy should be chosen and assured in relation to the biological and ecological processes investigated. Besides, standardized methods and protocols are strictly necessary to meet the FAIR (findability, accessibility, interoperability, and reusability) data principles for the stewardship of habitat mapping and biodiversity, biomass, and growth data.

Large-area image acquisition techniques are essential in underwater investigations: high-resolution 3D image-based reconstructions have improved coral reef monitoring by enabling novel seascape ecological analysis. Artificial intelligence (AI) offers methods for significantly accelerating image data interpretation, such as automatically recognizing, enumerating, and measuring organisms. However, the rapid proliferation of these technological achievements has led to a relative lack of standardization of methods. Remarkably, there are notable differences in procedures for generating human and AI annotations, and there is also a scarcity of publicly available datasets and shared machine-learning models. The lack of standard procedures makes it challenging to compare and reproduce scientific findings. One way to overcome this problem is to make the most used platforms by coral reef scientists interoperable so that the analyses can all be exported into a common format. This paper introduces functionality to promote interoperability between three popular open-source software tools dedicated to the digital study of coral reefs: TagLab, CoralNet, and Viscore. As users of each platform may have different analysis pipelines, we discuss several workflows for managing and processing point and area annotations, improving collaboration among these tools. Our work sets the foundation for a more seamless ecosystem that maintains the established investigation procedures of various laboratories but allows for easier result sharing.

We present a novel interactive framework for improving 3D reconstruction starting from incomplete or noisy results obtained through image-based reconstruction algorithms. The core idea is to enable the user to provide localized hints on the curvature of the surface, which are turned into constraints during an energy minimization reconstruction. To make this task simple, we propose two algorithms. The first is a multi-view segmentation algorithm that allows the user to propagate the foreground selection of one or more images both to all the images of the input set and to the 3D points, to accurately select the part of the scene to be reconstructed. The second is a fast GPU-based algorithm for the reconstruction of smooth surfaces from multiple views, which incorporates the hints provided by the user. We show that our framework can turn a poor-quality reconstruction produced with state of the art image-based reconstruction methods into a high- quality one.

Close-Range Photogrammetry (CRP) and Reflectance Transformation Imaging (RTI) are two of the most used image-based techniques when documenting and analyzing Cultural Heritage (CH) objects. Nevertheless, their potential impact in supporting study and analysis of conservation status of CH assets is reduced as they remain mostly applied and analyzed separately. This is mostly because we miss easy-to-use tools for of a spatial registration of multimodal data and features for joint visualisation gaps. The aim of this paper is to describe a complete framework for an effective data fusion and to present a user friendly viewer enabling the joint visual analysis of 2D/3D data and RTI images. This contribution is framed by the on-going implementation of automatic multimodal registration (3D, 2D RGB and RTI) into a collaborative web platform (AIOLI) enabling the management of hybrid representations through an intuitive visualization framework and also supporting semantic enrichment through spatialized 2D/3D annotations.

This study tackles the image color to gray conversion problem. The aim was to understand the conversion qualities that can improve the accuracy of results when the grayscale conversion is applied as a pre-processing step in the context of vision algorithms, and in particular dense stereo matching. We evaluated many different state of the art color to grayscale conversion algorithms. We also propose an ad-hoc adaptation of the most theoretically promising algorithm, which we call Multi-Image Decolorize (MID). This algorithm comes from an in-depth analysis of the existing conversion solutions and consists of a multi-image extension of the algorithm by Grundland and Dodgson (The decolorize algorithm for contrast enhancing, color to grayscale conversion, Tech. Rep. UCAM-CL-TR-649, University of Cambridge, 2005 ) which is based on predominant component analysis. In addition, two variants of this algorithm have been proposed and analyzed: one with standard unsharp masking and another with a chromatic weighted unsharp masking technique (Nowak and Baraniuk in IEEE Trans Image Process 7(7):1068–1074, 1998 ) which enhances the local contrast as shown in the approach by Smith et al. (Comput Graph Forum 27(2), 2008 ). We tested the relative performances of this conversion with respect to many other solutions, using the StereoMatcher test suite (Scharstein and Szeliski in Int J Comput Vis 47(1–3):7–42, 2002 ) with a variety of different datasets and different dense stereo matching algorithms. The results show that the overall performance of the proposed MID conversion are good and the reported tests provided useful information and insights on how to design color to gray conversion to improve matching performance. We also show some interesting secondary results such as the role of standard unsharp masking vs . chromatic unsharp masking in improving correspondence matching.

This study tackles the image color to gray conversion problem. The aim was to understand the conversion qualities that can improve the accuracy of results when the grayscale conversion is applied as a pre-processing step in the context of vision algorithms, and in particular dense stereo matching. We evaluated many different state of the art color to grayscale conversion algorithms. We also propose an ad-hoc adaptation of the most theoretically promising algorithm, which we call Multi-Image Decolorize (MID). This algorithm comes from an in-depth analysis of the existing conversion solutions and consists of a multi-image extension of the algorithm by Grundland and Dodgson (The decolorize algorithm for contrast enhancing, color to grayscale conversion, Tech. Rep. UCAM-CL-TR-649, University of Cambridge, 2005) which is based on predominant component analysis. In addition, two variants of this algorithm have been proposed and analyzed: one with standard unsharp masking and another with a chromatic weighted unsharp masking technique (Nowak and Baraniuk in IEEE Trans Image Process 7(7):1068–1074, 1998) which enhances the local contrast as shown in the approach by Smith et al. (Comput Graph Forum 27(2), 2008). We tested the relative performances of this conversion with respect to many other solutions, using the StereoMatcher test suite (Scharstein and Szeliski in Int J Comput Vis 47(1–3):7–42, 2002) with a variety of different datasets and different dense stereo matching algorithms. The results show that the overall performance of the proposed MID conversion are good and the reported tests provided useful information and insights on how to design color to gray conversion to improve matching performance. We also show some interesting secondary results such as the role of standard unsharp masking vs. chromatic unsharp masking in improving correspondence matching.

Fully automatic semantic segmentation of highly specific semantic classes and complex shapes may not meet the accuracy standards demanded by scientists. In such cases, human-centered AI solutions, able to assist operators while preserving human control over complex tasks, are a good trade-off to speed up image labeling while maintaining high accuracy levels. TagLab is an open-source AI-assisted software for annotating large orthoimages which takes advantage of different degrees of automation; it speeds up image annotation from scratch through assisted tools, creates custom fully automatic semantic segmentation models, and, finally, allows the quick edits of automatic predictions. Since the orthoimages analysis applies to several scientific disciplines, TagLab has been designed with a flexible labeling pipeline. We report our results in two different scenarios, marine ecology, and architectural heritage.