Demo-Pose: Depth-Monocular Modality Fusion For Object Pose Estimation
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| Format: | Preprint |
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2026
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| _version_ | 1866914429884432384 |
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| author | Agarwal, Rachit Joshi, Abhishek Chalasani, Sathish Kim, Woo Jin |
| author_facet | Agarwal, Rachit Joshi, Abhishek Chalasani, Sathish Kim, Woo Jin |
| contents | Object pose estimation is a fundamental task in 3D vision with applications in robotics, AR/VR, and scene understanding. We address the challenge of category-level 9-DoF pose estimation (6D pose + 3Dsize) from RGB-D input, without relying on CAD models during inference. Existing depth-only methods achieve strong results but ignore semantic cues from RGB, while many RGB-D fusion models underperform due to suboptimal cross-modal fusion that fails to align semantic RGB cues with 3D geometric representations. We propose DeMo-Pose, a hybrid architecture that fuses monocular semantic features with depth-based graph convolutional representations via a novel multimodal fusion strategy. To further improve geometric reasoning, we introduce a novel Mesh-Point Loss (MPL) that leverages mesh structure during training without adding inference overhead. Our approach achieves real-time inference and significantly improves over state-of-the-art methods across object categories, outperforming the strong GPV-Pose baseline by 3.2\% on 3D IoU and 11.1\% on pose accuracy on the REAL275 benchmark. The results highlight the effectiveness of depth-RGB fusion and geometry-aware learning, enabling robust category-level 3D pose estimation for real-world applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_27533 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Demo-Pose: Depth-Monocular Modality Fusion For Object Pose Estimation Agarwal, Rachit Joshi, Abhishek Chalasani, Sathish Kim, Woo Jin Computer Vision and Pattern Recognition Artificial Intelligence I.2.10 Object pose estimation is a fundamental task in 3D vision with applications in robotics, AR/VR, and scene understanding. We address the challenge of category-level 9-DoF pose estimation (6D pose + 3Dsize) from RGB-D input, without relying on CAD models during inference. Existing depth-only methods achieve strong results but ignore semantic cues from RGB, while many RGB-D fusion models underperform due to suboptimal cross-modal fusion that fails to align semantic RGB cues with 3D geometric representations. We propose DeMo-Pose, a hybrid architecture that fuses monocular semantic features with depth-based graph convolutional representations via a novel multimodal fusion strategy. To further improve geometric reasoning, we introduce a novel Mesh-Point Loss (MPL) that leverages mesh structure during training without adding inference overhead. Our approach achieves real-time inference and significantly improves over state-of-the-art methods across object categories, outperforming the strong GPV-Pose baseline by 3.2\% on 3D IoU and 11.1\% on pose accuracy on the REAL275 benchmark. The results highlight the effectiveness of depth-RGB fusion and geometry-aware learning, enabling robust category-level 3D pose estimation for real-world applications. |
| title | Demo-Pose: Depth-Monocular Modality Fusion For Object Pose Estimation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.10 |
| url | https://arxiv.org/abs/2603.27533 |