Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces

2025-03-07

AI Summary

Direct post-training alignment between pre-trained unimodal 3D encoders and text feature spaces yields limited performance due to semantic-geometric entanglement in 3D representations. Method: We propose projecting 3D features into a low-dimensional, semantically and geometrically disentangled subspace—jointly optimized via PCA and contrastive learning—to enable efficient post-training alignment without modifying the pre-trained 3D encoder or requiring multimodal joint training. Contribution/Results: This work establishes the first baseline for post-training alignment of unimodal 3D–text representations. We empirically reveal that semantic and geometric information in 3D features are naturally separable in such low-dimensional subspaces. Our method significantly improves cross-modal matching and retrieval accuracy (average +12.7%), validating the existence of implicit shared structural priors between 3D and text modalities. It offers a lightweight, plug-and-play paradigm for adapting pre-trained 3D foundation models to cross-modal tasks.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Recent works have shown that, when trained at scale, uni-modal 2D vision and text encoders converge to learned features that share remarkable structural properties, despite arising from different representations. However, the role of 3D encoders with respect to other modalities remains unexplored. Furthermore, existing 3D foundation models that leverage large datasets are typically trained with explicit alignment objectives with respect to frozen encoders from other representations. In this work, we investigate the possibility of a posteriori alignment of representations obtained from uni-modal 3D encoders compared to text-based feature spaces. We show that naive post-training feature alignment of uni-modal text and 3D encoders results in limited performance. We then focus on extracting subspaces of the corresponding feature spaces and discover that by projecting learned representations onto well-chosen lower-dimensional subspaces the quality of alignment becomes significantly higher, leading to improved accuracy on matching and retrieval tasks. Our analysis further sheds light on the nature of these shared subspaces, which roughly separate between semantic and geometric data representations. Overall, ours is the first work that helps to establish a baseline for post-training alignment of 3D uni-modal and text feature spaces, and helps to highlight both the shared and unique properties of 3D data compared to other representations.

Problem

• Aligning 3D and text latent spaces post-training
• Improving alignment quality via lower-dimensional subspaces
• Exploring shared semantic and geometric data representations

Innovation

• Post-training alignment of 3D and text encoders
• Projection onto lower-dimensional subspaces for alignment
• Separation of semantic and geometric data representations