Understanding and monitoring the surrounding environment increasingly rely on its 3D representations. However, the often high costs of 3D data equipment limit its wide usage, and low-cost solutions are in demand. Here, we propose a novel approach based on spherical stereo videos captured with a known baseline (distance between the cameras) for a low-cost and efficient 3D point cloud reconstruction. In a forest environment, we evaluated 1) the influence of baseline length on point cloud quality and 2) the suitability of the generated point clouds for extracting primary forest attributes (tree position and diameter). Our results show that the proposed approach allows for feasible 3D reconstruction of complex forest plots. The highest point cloud quality was achieved with a baseline of 60 cm. This setup enabled the correct detection of more than 65% of the trees within the forest plots, producing an average tree position error between 30 and 50 cm and clearly outperforming other setups. A Multiscale Model-to-Model Cloud (M3C2) analysis showed signed distances between the generated point cloud and the reference data with zero mean and 1 m standard deviation. We demonstrate that the proposed approach can be a valuable low-cost solution for 3D point cloud reconstruction, facilitating forest assessment and monitoring.

This paper explores the generation of “realistic” 3D representations of individual trees to enhance visualizations of forest simulation tool outcomes. By leveraging remote sensing data, we aim to capture individual tree features and characteristics accurately, linking them to dynamic simulations of forest structures and composition. Employing a deep learning approach, we train models on existing 3D scanned data to produce diverse and realistic visual representations of specific tree species. Our method addresses the limitations of existing synthetic tree generation techniques, which often overlook species-specific characteristics. Our approach emphasizes the generation of diverse tree forms, accounting for differences in trunk shape, canopy size, and branching structures. The resulting 3D data offers potential applications for realistic future forest visualizations and improved data augmentation in tree classification models, ultimately contributing to the creation of virtual forests that represent rich species diversity.

Purpose of the Review: We aim to present virtual forest visualization in an accessible manner to help a broad audience understand this concept along with spatial data integration to create digital environments that closely replicate real-world forests. Such efforts are motivated by a multitude of reasons, from creating a virtual science laboratory to guiding real-world applications. The digital reconstruction of forests is an evolving field in forestry, with close connections to computer science and data science. The key considerations in visualization are also explained in detail which are crucial part of visualization like Input and output data along with challenges and constraints. Findings: Recent advancements in digital sensing technologies have improved forest visualizations considerably. Data from techniques such as LiDAR and photogrammetry, along with other forest information, have facilitated the creation of highly detailed and accurate virtual forest models. These technologies not only capture the geometric structure of forests, but also enable the interactive exploration of different layers, from the canopy to the forest floor. The integration of these methods with Geographic Information Systems (GIS) and various user interfaces has further enhanced the spatial accuracy and contextual relevance of virtual forests. A major breakthrough in forest visualization is the incorporation of Extended Reality (XR) technologies, including Augmented (AR), Virtual (VR), and Mixed Reality (MR). These tools enable users to experience forest environments in a fully immersive way, without the need for physical presence. Given that some forests are prohibitively difficult to reach and navigate, and that some experiences can be greatly enriched by overlaying virtual elements onto the real world, XR experiences offer a valuable alternative to traditional data visualization approaches. Additionally, we also present four case studies to showcase the applications of visualization in the European forest context which can be applicable in the global context. Summary: We examine key trends, developments, and challenges according to the global research in the use of 2D and 3D visualization for virtual forest experiences and present selected case studies. We describe the potential of virtual forest visualization for applications in forest management, conservation, education, and research. We also present an opportunity to utilize and develop further the scope of visualization using these latest technologies in forestry globally.

The growing popularity of applications based on 3D rendering, such as visual effects, gaming, augmented and virtual reality, calls for the development of new solutions for the delivery of 3D objects, in particular textures. The format of texture images, which capture the characteristics of materials, has to address two constraints. First, the delivery on the Internet imposes a reduction of the image size. Second, because of memory limitations, the processing by the rendering engine is done in the GPU by extracting small areas of the image only. The format of texture images should thus enable the random-access feature for independent processing of small blocks of the images, called texels, which negatively affects the texture compression performance and, therefore, the network delivery. We propose 3CPS, a novel solution for the compression and delivery of texture images. In 3CPS, the texture image is compressed three times: first, at the authoring side, by a traditional texture compression technique; second, still at the authoring side, by a state-of-the-art image compression technique for better network delivery; third, at the client side, the received image is re-compressed by a texture image technique for better GPU processing. Our original idea leverages the fact that the image at the client side has already been converted into a format that can be easily transformed by the client for GPU processing. The last compression is thus expected to be much faster than usual. In this paper, we introduce this concept, propose a fast and efficient algorithm for the last re-compression, and demonstrate the advantages of our solution by performing an extensive evaluation on real data sets. In particular, we show that 3CPS competes well with Basis supercompression, which is evaluated for the first time.