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Item Automatic Image-Based Coral Polyp Analysis through Multi-View Instance Segmentation(The Eurographics Association, 2025) Dutta, Somnath; Pavoni, Gaia; Corsini, Massimiliano; Ganovelli, Fabio; Cignoni, Paolo; Rossi, Paolo; Cenni, Elena; Simonini, Roberto; Grassi, Francesca; Cassanelli, Davide; Cattini, Stefano; Rovati, Luigi; Capra, Alessandro; Castagnetti, Cristina; Günther, Tobias; Montazeri, ZahraWe present an automated framework for counting and measuring the polyps of Cladocora caespitosa, a Mediterranean reefbuilding coral. To our knowledge, the most practical method for counting polyps currently involves ecologists' visual inspection of a 3D model. However, measuring polyps from the model can lead to inaccuracies due to distortions in the reconstruction. Our method integrates deep learning-based instance segmentation on 2D images with 3D models for unique polyp identification, ensuring precise biometric extraction. The proposed pipeline automates polyp detection, counting, and measurement while overcoming the limitations of manual in situ methods. Laboratory validation demonstrates its accuracy and efficiency, paving the way for scalable, high-resolution phenotyping, and field monitoring of Mediterranean coral populations.Item NOVA-3DGS: No-reference Objective VAlidation for 3D Gaussian Splatting(The Eurographics Association, 2025) Piras, Valentina; Bonatti, Amedeo Franco; Maria, Carmelo De; Cignoni, Paolo; Banterle, Francesco; Günther, Tobias; Montazeri, ZahraIn recent years, radiance field methods, and in particular 3D Gaussian Splatting (3DGS), have distinguished themselves in the field of image-based rendering and scene reconstruction techniques, gaining significant success in academia and being cited in numerous research papers. Like other methods, 3DGS requires a large and diverse dataset of images for network training as a fundamental step to ensure effectiveness and high-quality results. Consequently, the acquisition phase is highly time-consuming, especially considering that a portion of the acquired dataset is not actually used for training but is reserved for testing. This is necessary because all commonly used metrics for evaluating the quality of 3D reconstructions, such as PSNR and SSIM, are reference-based metrics; i.e., requiring a ground truth. In this work, we present NOVA, a study focused on no-reference evaluation of 3DGS renders, based on key metrics in this field: PSNR and SSIM.