Light field angular super-resolution (LFASR) aims to reconstruct densely sampled angular views from sparsely captured inputs, enabling high-fidelity rendering, refocusing, and depth estimation. In this paper, we propose a novel LFASR framework that employs a tri-visualization feature extraction strategy, which jointly processes Sub-Aperture Images (SAIs), Epipolar Plane Images (EPIs), and Macro-Pixel Images (MacroPIs) to comprehensively exploit the spatial-angular structure of light fields. These complementary representations are processed in parallel to extract diverse and informative features, which are then refined through a deep spatial aggregation module composed of residual blocks. The proposed pipeline consists of three key stages: Early Feature Extraction (EFE), Advanced Feature Refinement (AFR), and Angular Super-Resolution (ASR). Extensive experiments on both synthetic and real-world …

Accurately estimating the 3D layout of rooms is a crucial task in computer vision, with potential applications in robotics, augmented reality, and interior design. This paper proposes a novel model, PanoTPS-Net, to estimate room layout from a single panorama image. Leveraging a Convolutional Neural Network (CNN) and incorporating a Thin Plate Spline (TPS) spatial transformation, the architecture of PanoTPS-Net is divided into two stages: First, a convolutional neural network extracts the high-level features from the input images, allowing the network to learn the spatial parameters of the TPS transformation. Second, the TPS spatial transformation layer is generated to warp a reference layout to the required layout based on the predicted parameters. This unique combination empowers the model to properly predict room layouts while also generalizing effectively to both cuboid and non-cuboid layouts. Extensive experiments on publicly available datasets and comparisons with state-of-the-art methods demonstrate the effectiveness of the proposed method. The results underscore the model’s accuracy in room layout estimation and emphasize the compatibility between the TPS transformation and panorama images. The robustness of the model in handling both cuboid and non-cuboid room layout estimation is evident with a 3DIoU value of 85.49, 86.16, 81.76, and 91.98 on PanoContext, Stanford-2D3D, Matterport3DLayout, and ZInD datasets, respectively. The source code is available at: https://github.com/HatemHosam/PanoTPS_Net.

Canal rehabilitation improves water management and agricultural productivity but requires balancing immediate benefits with long-term sustainability risks. Unlike previous studies that focused mainly on water seepage and groundwater effects, this research takes a comprehensive approach by integrating environmental, social, and economic factors to create strategic guidelines for sustainable canal projects. This holistic method ensures that water infrastructure improvements optimize performance while maintaining ecological balance, supporting communities, and addressing water security challenges amid climate change and increasing demand. This research introduces a novel approach for conducting quantitative risk assessments of concrete-lined canals by combining Fuzzy logic, the Analytic Hierarchy Process (FAHP), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This integrated framework allows for the effective identification and prioritization of key risk factors associated with concrete canal lining projects. To validate the methodology, the study applied it to a case study in Egypt. Risk factors were categorized and analyzed across three primary domains: environmental, economic, and social. Using the FAHP-TOPSIS approach, the study assigned weighted values to individual risk factors within each domain, enabling a comparative evaluation of risk indices and a systematic ranking of environmental, economic, and social considerations. The findings revealed that environmental risk domain had higher index values compared to economic and social domain. Notably, groundwater depletion emerged as a critical factor with a significant impact on the overall risk index. The research employs bow-tie analysis to thoroughly investigate the five most critical risk factors.