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Proceedings of the AAAI Conference on Artificial Intelligence

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3DAlign-DAER: Dynamic Attention Policy and Efficient Retr...
2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Yijia Fan Sun Yat-Sen University
  • Jusheng Zhang Sun Yat-Sen University
  • Kaitong Cai Sun Yat-Sen University
  • Jing Yang Sun Yat-Sen University
  • Jian Wang Snap Inc.
  • Keze Wang Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing

DOI:

https://doi.org/10.1609/aaai.v40i1.36987

Abstract

Despite recent advancements in 3D-text cross-modal alignment, existing state-of-the-art methods still struggle to align fine-grained textual semantics with detailed geometric structures, and their alignment performance degrades significantly when scaling to large-scale 3D databases. To overcome this limitation, we introduce 3DAlign-DAER, a unified framework designed to align text and 3D geometry via the proposed dynamic attention policy and the efficient retrieval strategy, capturing subtle correspondences for diverse cross-modal retrieval and classification tasks. Specifically, during the training, our proposed dynamic attention policy (DAP) employs the Hierarchical Attention Fusion (HAF) module to represent the alignment as learnable fine-grained token-to-point attentions. To optimize these attentions across different tasks and geometric hierarchies, our DAP further exploits the Monte Carlo tree search to dynamically calibrate HAF attention weights via a hybrid reward signal and further enhances the alignment between textual descriptions and local 3D geometry. During the inference, our 3DAlign-DAER introduces an Efficient Retrieval Strategy (ERS) to leverage efficient hierarchical searching in the large-scale embedding spaces, outperforming traditional methods (eg, KNN) in accuracy and efficiency. Furthermore, to facilitate text-3D alignment research and train our 3DAlign-DAER, we construct Align3D-2M, a large-scale dataset featuring 2M text-3D pairs, to provide sufficient fine-grained cross-modal annotations. Extensive and comprehensive experiments demonstrate the superior performance of our 3DAlign-DAER on diverse benchmarks.

How to Cite

Fan, Y., Zhang, J., Cai, K., Yang, J., Wang, J., & Wang, K. (2026). 3DAlign-DAER: Dynamic Attention Policy and Efficient Retrieval Strategy for Fine-grained 3D-Text Alignment at Scale. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 265–273. https://doi.org/10.1609/aaai.v40i1.36987

Issue

Section

AAAI Technical Track on Application Domains I

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