{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/solving-zero-shot-3d-visual-grounding-as","title":"Solving Zero-Shot 3D Visual Grounding as Constraint Satisfaction Problems","arxiv_id":"2411.14594","date":"2024-11-21","proceeding":null,"authors":["Qihao Yuan","Jiaming Zhang","Kailai Li","Rainer Stiefelhagen"],"abstract":"3D visual grounding (3DVG) aims to locate objects in a 3D scene with natural language descriptions. Supervised methods have achieved decent accuracy, but have a closed vocabulary and limited language understanding ability. Zero-shot methods mostly utilize large language models (LLMs) to handle natural language descriptions, yet suffer from slow inference speed. To address these problems, in this work, we propose a zero-shot method that reformulates the 3DVG task as a Constraint Satisfaction Problem (CSP), where the variables and constraints represent objects and their spatial relations, respectively. This allows a global reasoning of all relevant objects, producing grounding results of both the target and anchor objects. Moreover, we demonstrate the flexibility of our framework by handling negation- and counting-based queries with only minor extra coding efforts. Our system, Constraint Satisfaction Visual Grounding (CSVG), has been extensively evaluated on the public datasets ScanRefer and Nr3D datasets using only open-source LLMs. Results show the effectiveness of CSVG and superior grounding accuracy over current state-of-the-art zero-shot 3DVG methods with improvements of $+7.0\\%$ (Acc@0.5 score) and $+11.2\\%$ on the ScanRefer and Nr3D datasets, respectively. The code of our system is publicly available at https://github.com/sunsleaf/CSVG.","url_abs":"https://arxiv.org/abs/2411.14594v1","url_pdf":"https://arxiv.org/pdf/2411.14594v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"solving-zero-shot-3d-visual-grounding-as","repo_url":"https://github.com/sunsleaf/csvg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-visual-grounding","task_name":"3D visual grounding"},{"task_slug":"negation","task_name":"Negation"},{"task_slug":"visual-grounding","task_name":"Visual Grounding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2411.14594","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}