@inproceedings{li-etal-2026-answering,
    title = "Answering Cross-Dimensional Geometric Visual Questions by Multi-constraint Spatial Reasoning",
    author = "Li, Dongling  and
      Chen, Qi  and
      Yu, Jianxing  and
      Lai, Hanjiang  and
      Rao, Yanghui  and
      Chen, Wenqing  and
      Yin, Jian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1656/",
    pages = "33093--33111",
    ISBN = "979-8-89176-395-1",
    abstract = "This paper focuses on the task of answering complex visual questions that involve cross-dimensional (like 2D to 3D) spatial reasoning. This task (called SpatialQA) can enhance the machine{'}s spatial cognitive abilities in ``plane representation - space reconstruction - semantic inference,'' having great application value. Existing methods often only recognize 1-D visual objects and relations, but they lack the ability to represent in a cross-dimensional space and fail to grasp structured geometric knowledge such as face-face topology and texture details. That would cause problems such as texture misalignment and topological confusion, leading to error accumulation and incorrect answers. To address this problem, we propose a new method with good cross-dimensional reasoning capabilities. In detail, we first analyze the input image, capturing its relations in the 2D plane. To derive the topological relations in the 3D space, we employ a dual-channel augmentation technique to retrieve topological isomorphic examples and geometric rules, supplementing the missing but crucial reasoning clues. We then design a multi-perspective verifier to find the inconsistencies of the macroscopic outlines, eliminating incorrect options. Based on visual clues, we develop a question-guided detector to analyze the texture details and relations of each surface finely, capturing inconsistencies in a micro level. That can correct the reasoning bias to derive the right answer. Moreover, we create a large-scale dataset with 22,483 samples to conduct evaluations. The results show the effectiveness of our method."
}