{"paper":{"title":"SHREC 2025: Retrieval of Optimal Objects for Multi-modal Enhanced Language and Spatial Assistance (ROOMELSA)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anh-Duong Tran, Bao Huynh Thai, Dat Phan Thanh, Dinh-Khoi Vo, Duc-Vu Nguyen, Hien-Long Le-Hoang, Hoang-Phuc Nguyen, Hoang Tran Van, Huy Nguyen Phong, Kim Nguyen, Long Le Bao, Man-Khoi Tran, Minh-Chinh Nguyen, Minh-Huy Le-Hoang, Minh Nguyen Anh, Minh-Quan Ho, Minh-Triet Tran, Ngoc-Long Tran, Nguyen-Khang Le, Nhan Nguyen Viet Thien, Phat Nguyen Thuan, Phu-Hoa Pham, Quang-Thuc Nguyen, Quan Nguyen Hung, Tam V. Nguyen, Thai Hoang Minh, Thang Nguyen Tien, Tien Huynh Viet, Trong-Thuan Nguyen, Trung-Nghia Le, Van-Loc Nguyen, Viet-Tham Huynh, Vinh-Tiep Nguyen","submitted_at":"2025-08-12T09:36:05Z","abstract_excerpt":"Recent 3D retrieval systems are typically designed for simple, controlled scenarios, such as identifying an object from a cropped image or a brief description. However, real-world scenarios are more complex, often requiring the recognition of an object in a cluttered scene based on a vague, free-form description. To this end, we present ROOMELSA, a new benchmark designed to evaluate a system's ability to interpret natural language. Specifically, ROOMELSA attends to a specific region within a panoramic room image and accurately retrieves the corresponding 3D model from a large database. In addi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.08781","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.08781/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}