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.

通过两个无监督方法，我们提出了 $I2I$ 和 $(I2L)^2$，利用 CLIP 关于文本和 2D 数据的知识，计算两个 3D 样本之间的神经感知相似度，从而在没有文本描述的情况下增强对比的文本 - 图像 - 3D 对齐，通过自定义损失函数和硬负样本挖掘建立多模态对比流程，我们的方法在 3D 分类和跨模态检索基准测试方面取得了与之前方法相媲美甚至更优的性能，显著改善了图像到形状和形状到图像的检索效果。