Horizon 1 1 17 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES IET Computer Vision e70036 [18 p.] botany computer vision image classification image processing image representation learning (artificial intelligence) neural net architecture neural nets object recognition 2025 fdi:010095035 ENG IET Computer Vision 1751-9632 ISI:001576587900001 1 10.1049/cvi2.70036 https://www.documentation.ird.fr/hor/fdi:010095035 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-11/010095035.pdf 19 Horizon (IRD) We introduce the shape-image multimodal network (SIM-Net), a novel 2D image classification architecture that integrates 3D point cloud representations inferred directly from RGB images. Our key contribution lies in a pixel-to-point transformation that converts 2D object masks into 3D point clouds, enabling the fusion of texture-based and geometric features for enhanced classification performance. SIM-Net is particularly well-suited for the classification of digitised herbarium specimens-a task made challenging by heterogeneous backgrounds, nonplant elements, and occlusions that compromise conventional image-based models. To address these issues, SIM-Net employs a segmentation-based preprocessing step to extract object masks prior to 3D point cloud generation. The architecture comprises a CNN encoder for 2D image features and a PointNet-based encoder for geometric features, which are fused into a unified latent space. Experimental evaluations on herbarium datasets demonstrate that SIM-Net consistently outperforms ResNet101, achieving gains of up to 9.9% in accuracy and 12.3% in F-score. It also surpasses several transformer-based state-of-the-art architectures, highlighting the benefits of incorporating 3D structural reasoning into 2D image classification tasks. 122 ; 076 UR209