Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignment. While such reasoning-centric approaches have been proven effective in language and vision tasks, their extension to 3D remains underdeveloped. CoRe3D introduces a unified 3D understanding and generation reasoning framework that jointly operates over semantic and spatial abstractions, enabling high-level intent inferred from language to directly guide low-level 3D content formation. Central to this design is a spatially grounded reasoning representation that decomposes 3D latent space into localized regions, allowing the model to reason over geometry in a compositional and procedural manner. By tightly coupling semantic chain-of-thought inference with structured spatial reasoning, CoRe3D produces 3D outputs that exhibit strong local consistency and faithful alignment with linguistic descriptions.
@article{arxiv.2512.12768,
title = {CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence},
author = {Tianjiao Yu and Xinzhuo Li and Yifan Shen and Yuanzhe Liu and Ismini Lourentzou},
journal= {arXiv preprint arXiv:2512.12768},
year = {2026}
}