@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://aclanthology.org/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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2026-answering">
<titleInfo>
<title>Answering Cross-Dimensional Geometric Visual Questions by Multi-constraint Spatial Reasoning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dongling</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jianxing</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hanjiang</namePart>
<namePart type="family">Lai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yanghui</namePart>
<namePart type="family">Rao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wenqing</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jian</namePart>
<namePart type="family">Yin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<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.</abstract>
<identifier type="citekey">li-etal-2026-answering</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.1656/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>33093</start>
<end>33111</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Answering Cross-Dimensional Geometric Visual Questions by Multi-constraint Spatial Reasoning
%A Li, Dongling
%A Chen, Qi
%A Yu, Jianxing
%A Lai, Hanjiang
%A Rao, Yanghui
%A Chen, Wenqing
%A Yin, Jian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-answering
%X 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.
%U https://aclanthology.org/2026.findings-acl.1656/
%P 33093-33111
Markdown (Informal)
[Answering Cross-Dimensional Geometric Visual Questions by Multi-constraint Spatial Reasoning](https://aclanthology.org/2026.findings-acl.1656/) (Li et al., Findings 2026)
ACL
- Dongling Li, Qi Chen, Jianxing Yu, Hanjiang Lai, Yanghui Rao, Wenqing Chen, and Jian Yin. 2026. Answering Cross-Dimensional Geometric Visual Questions by Multi-constraint Spatial Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 33093–33111, San Diego, California, United States. Association for Computational Linguistics.