SHREC 2025: Retrieval of Optimal Objects for Multi-modal Enhanced Language and Spatial Assistance (ROOMELSA)
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911102429822976 |
|---|---|
| author | Nguyen, Trong-Thuan Huynh, Viet-Tham Nguyen, Quang-Thuc Nguyen, Hoang-Phuc Bao, Long Le Minh, Thai Hoang Anh, Minh Nguyen Tien, Thang Nguyen Thuan, Phat Nguyen Phong, Huy Nguyen Thai, Bao Huynh Nguyen, Vinh-Tiep Nguyen, Duc-Vu Pham, Phu-Hoa Le-Hoang, Minh-Huy Le, Nguyen-Khang Nguyen, Minh-Chinh Ho, Minh-Quan Tran, Ngoc-Long Le-Hoang, Hien-Long Tran, Man-Khoi Tran, Anh-Duong Nguyen, Kim Hung, Quan Nguyen Thanh, Dat Phan Van, Hoang Tran Viet, Tien Huynh Thien, Nhan Nguyen Viet Vo, Dinh-Khoi Nguyen, Van-Loc Le, Trung-Nghia Nguyen, Tam V. Tran, Minh-Triet |
| author_facet | Nguyen, Trong-Thuan Huynh, Viet-Tham Nguyen, Quang-Thuc Nguyen, Hoang-Phuc Bao, Long Le Minh, Thai Hoang Anh, Minh Nguyen Tien, Thang Nguyen Thuan, Phat Nguyen Phong, Huy Nguyen Thai, Bao Huynh Nguyen, Vinh-Tiep Nguyen, Duc-Vu Pham, Phu-Hoa Le-Hoang, Minh-Huy Le, Nguyen-Khang Nguyen, Minh-Chinh Ho, Minh-Quan Tran, Ngoc-Long Le-Hoang, Hien-Long Tran, Man-Khoi Tran, Anh-Duong Nguyen, Kim Hung, Quan Nguyen Thanh, Dat Phan Van, Hoang Tran Viet, Tien Huynh Thien, Nhan Nguyen Viet Vo, Dinh-Khoi Nguyen, Van-Loc Le, Trung-Nghia Nguyen, Tam V. Tran, Minh-Triet |
| contents | 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 addition, ROOMELSA includes over 1,600 apartment scenes, nearly 5,200 rooms, and more than 44,000 targeted queries. Empirically, while coarse object retrieval is largely solved, only one top-performing model consistently ranked the correct match first across nearly all test cases. Notably, a lightweight CLIP-based model also performed well, although it struggled with subtle variations in materials, part structures, and contextual cues, resulting in occasional errors. These findings highlight the importance of tightly integrating visual and language understanding. By bridging the gap between scene-level grounding and fine-grained 3D retrieval, ROOMELSA establishes a new benchmark for advancing robust, real-world 3D recognition systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SHREC 2025: Retrieval of Optimal Objects for Multi-modal Enhanced Language and Spatial Assistance (ROOMELSA) Nguyen, Trong-Thuan Huynh, Viet-Tham Nguyen, Quang-Thuc Nguyen, Hoang-Phuc Bao, Long Le Minh, Thai Hoang Anh, Minh Nguyen Tien, Thang Nguyen Thuan, Phat Nguyen Phong, Huy Nguyen Thai, Bao Huynh Nguyen, Vinh-Tiep Nguyen, Duc-Vu Pham, Phu-Hoa Le-Hoang, Minh-Huy Le, Nguyen-Khang Nguyen, Minh-Chinh Ho, Minh-Quan Tran, Ngoc-Long Le-Hoang, Hien-Long Tran, Man-Khoi Tran, Anh-Duong Nguyen, Kim Hung, Quan Nguyen Thanh, Dat Phan Van, Hoang Tran Viet, Tien Huynh Thien, Nhan Nguyen Viet Vo, Dinh-Khoi Nguyen, Van-Loc Le, Trung-Nghia Nguyen, Tam V. Tran, Minh-Triet Computer Vision and Pattern Recognition 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 addition, ROOMELSA includes over 1,600 apartment scenes, nearly 5,200 rooms, and more than 44,000 targeted queries. Empirically, while coarse object retrieval is largely solved, only one top-performing model consistently ranked the correct match first across nearly all test cases. Notably, a lightweight CLIP-based model also performed well, although it struggled with subtle variations in materials, part structures, and contextual cues, resulting in occasional errors. These findings highlight the importance of tightly integrating visual and language understanding. By bridging the gap between scene-level grounding and fine-grained 3D retrieval, ROOMELSA establishes a new benchmark for advancing robust, real-world 3D recognition systems. |
| title | SHREC 2025: Retrieval of Optimal Objects for Multi-modal Enhanced Language and Spatial Assistance (ROOMELSA) |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.08781 |