DOMR: Establishing Cross-View Segmentation via Dense Object Matching
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912522188095488 |
|---|---|
| author | Liao, Jitong Gao, Yulu Huang, Shaofei Gao, Jialin Lei, Jie Liang, Ronghua Liu, Si |
| author_facet | Liao, Jitong Gao, Yulu Huang, Shaofei Gao, Jialin Lei, Jie Liang, Ronghua Liu, Si |
| contents | Cross-view object correspondence involves matching objects between egocentric (first-person) and exocentric (third-person) views. It is a critical yet challenging task for visual understanding. In this work, we propose the Dense Object Matching and Refinement (DOMR) framework to establish dense object correspondences across views. The framework centers around the Dense Object Matcher (DOM) module, which jointly models multiple objects. Unlike methods that directly match individual object masks to image features, DOM leverages both positional and semantic relationships among objects to find correspondences. DOM integrates a proposal generation module with a dense matching module that jointly encodes visual, spatial, and semantic cues, explicitly constructing inter-object relationships to achieve dense matching among objects. Furthermore, we combine DOM with a mask refinement head designed to improve the completeness and accuracy of the predicted masks, forming the complete DOMR framework. Extensive evaluations on the Ego-Exo4D benchmark demonstrate that our approach achieves state-of-the-art performance with a mean IoU of 49.7% on Ego$\to$Exo and 55.2% on Exo$\to$Ego. These results outperform those of previous methods by 5.8% and 4.3%, respectively, validating the effectiveness of our integrated approach for cross-view understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DOMR: Establishing Cross-View Segmentation via Dense Object Matching Liao, Jitong Gao, Yulu Huang, Shaofei Gao, Jialin Lei, Jie Liang, Ronghua Liu, Si Computer Vision and Pattern Recognition Cross-view object correspondence involves matching objects between egocentric (first-person) and exocentric (third-person) views. It is a critical yet challenging task for visual understanding. In this work, we propose the Dense Object Matching and Refinement (DOMR) framework to establish dense object correspondences across views. The framework centers around the Dense Object Matcher (DOM) module, which jointly models multiple objects. Unlike methods that directly match individual object masks to image features, DOM leverages both positional and semantic relationships among objects to find correspondences. DOM integrates a proposal generation module with a dense matching module that jointly encodes visual, spatial, and semantic cues, explicitly constructing inter-object relationships to achieve dense matching among objects. Furthermore, we combine DOM with a mask refinement head designed to improve the completeness and accuracy of the predicted masks, forming the complete DOMR framework. Extensive evaluations on the Ego-Exo4D benchmark demonstrate that our approach achieves state-of-the-art performance with a mean IoU of 49.7% on Ego$\to$Exo and 55.2% on Exo$\to$Ego. These results outperform those of previous methods by 5.8% and 4.3%, respectively, validating the effectiveness of our integrated approach for cross-view understanding. |
| title | DOMR: Establishing Cross-View Segmentation via Dense Object Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.04050 |