  In this study, we explore an alternative approach to enhance contrastive
text-image-3D alignment in the absence of textual descriptions for 3D objects.
We introduce two unsupervised methods, $I2I$ and $(I2L)^2$, which leverage CLIP
knowledge about textual and 2D data to compute the neural perceived similarity
between two 3D samples. We employ the proposed methods to mine 3D hard
negatives, establishing a multimodal contrastive pipeline with hard negative
weighting via a custom loss function. We train on different configurations of
the proposed hard negative mining approach, and we evaluate the accuracy of our
models in 3D classification and on the cross-modal retrieval benchmark, testing
image-to-shape and shape-to-image retrieval. Results demonstrate that our
approach, even without explicit text alignment, achieves comparable or superior
performance on zero-shot and standard 3D classification, while significantly
improving both image-to-shape and shape-to-image retrieval compared to previous
methods.
