arXiv subjects

Fatima Zahra Iguenfer

Publications and source records attributed to Fatima Zahra Iguenfer.

3 recordsLinked to original sources

arXiv2026

Point Cloud to Mesh Reconstruction: Methods, Trade-offs, and Implementation Guide

Reconstructing meshes from point clouds is a fundamental task in computer vision with applications spanning robotics, autonomous systems, and medical imaging. Selecting an appropriate learning-based method requires understanding trade-offs between computational efficiency, geometric accuracy, and output constraints. This paper categorizes over fifteen methods into five paradigms -- PointNet family, autoencoder architectures, deformation-based methods, point-move techniques, and primitive-based approaches -- and provides practical guidance for method selection. We contribute: (1) a decision framework mapping input/output requirements to suitable paradigms, (2) a failure mode analysis to assist practitioners in debugging implementations, (3) standardized comparisons on ShapeNet benchmarks, and (4) a curated list of maintained codebases with implementation resources. By synthesizing both theoretical foundations and practical considerations, this work serves as an entry point for practitioners and researchers new to learning-based 3D mesh reconstruction.

cs.CV↗

arXiv2025

AttriGen: Automated Multi-Attribute Annotation for Blood Cell Datasets

We introduce AttriGen, a novel framework for automated, fine-grained multi-attribute annotation in computer vision, with a particular focus on cell microscopy where multi-attribute classification remains underrepresented compared to traditional cell type categorization. Using two complementary datasets: the Peripheral Blood Cell (PBC) dataset containing eight distinct cell types and the WBC Attribute Dataset (WBCAtt) that contains their corresponding 11 morphological attributes, we propose a dual-model architecture that combines a CNN for cell type classification, as well as a Vision Transformer (ViT) for multi-attribute classification achieving a new benchmark of 94.62\% accuracy. Our experiments demonstrate that AttriGen significantly enhances model interpretability and offers substantial time and cost efficiency relative to conventional full-scale human annotation. Thus, our framework establishes a new paradigm that can be extended to other computer vision classification tasks by effectively automating the expansion of multi-attribute labels.

cs.CV↗

arXiv2025

NourID+: A Digital Energy Identity Framework for Efficient Subsidy Allocation in Morocco

We introduce NourID+, a digital energy identity framework that addresses Morocco's need for trusted energy subsidy allocation through authenticated digital identity integration. NourID+ creates a strong foundation for future subsidy programs by unifying three government-issued and digitalized credentials: Moroccan national identity cards (CIN), cadastral plans, and property ownership certificates are transformed into unique digital energy IDs (DE-IDs) that map authenticated identities with specific properties and their energy consumption patterns. The system supports three property ownership profiles: farmers (landowners), entrepreneurs (factory or company owners), and households (house owners), as energy consumption is directly related to land ownership. NourID+ provides dual access through a government portal allowing officials to process DE-ID generation requests, as well as a citizen portal for DE-ID usage and energy monitoring. Our framework supports CIN upload with facial biometric matching, automated property retrieval through government APIs, and government officer approval workflow for DE-ID generation. After evaluation of the system, we demonstrate a reduction in verification time from weeks to minutes, with 98% accuracy of document validation. The proposed solution allows for targeted subsidy allocation of electricity based on actual consumption needs rather than estimations, potentially improving the efficiency of Morocco's significant energy subsidy expenditure.

cs.CY↗