# DOMR: Establishing Cross-View Segmentation via Dense Object Matching

arXiv: https://arxiv.org/abs/2508.04050 · HTML: https://mlsanity.com/paper/2508.04050/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 56 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 30 of 38)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [Generative Novel View Synthesis with 3D-Aware Diffusion Models](https://mlsanity.com/paper/2304.02602/) (2023)
- [Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image](https://mlsanity.com/paper/2012.09855/) (2020)
- [K-Net: Towards Unified Image Segmentation](https://mlsanity.com/paper/2106.14855/) (2021)
- [DeMoN: Depth and Motion Network for Learning Monocular Stereo](https://mlsanity.com/paper/1612.02401/) (2016)
- [Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey](https://mlsanity.com/paper/2302.10035/) (2023)
- [GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences](https://mlsanity.com/paper/1912.05524/) (2019)
- [Consistent View Synthesis with Pose-Guided Diffusion Models](https://mlsanity.com/paper/2303.17598/) (2023)
- [Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos](https://mlsanity.com/paper/2104.07905/) (2021)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [A Survey on Instance Segmentation: State of the art](https://mlsanity.com/paper/2007.00047/) (2020)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image](https://mlsanity.com/paper/2203.09457/) (2022)
- [Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment](https://mlsanity.com/paper/2306.05526/) (2023)
- [YOLOE: Real-Time Seeing Anything](https://mlsanity.com/paper/2503.07465/) (2025)

## Built on by (most cited first, 2 of 2)

- [V2-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence](https://mlsanity.com/paper/2511.20886/) (2025)
- [VGGT-Segmentor: Geometry-Enhanced Cross-View Segmentation](https://mlsanity.com/paper/2604.13596/) (2026)

## Abstract (arXiv)

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→Exo and 55.2% on Exo→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.