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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.13692 |
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| _version_ | 1866908543731367936 |
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| author | Zeng, Yadan Zhou, Jiadong Li, Xiaohan Chen, I-Ming |
| author_facet | Zeng, Yadan Zhou, Jiadong Li, Xiaohan Chen, I-Ming |
| contents | Point cloud completion is essential for robotic perception, object reconstruction and supporting downstream tasks like grasp planning, obstacle avoidance, and manipulation. However, incomplete geometry caused by self-occlusion and sensor limitations can significantly degrade downstream reasoning and interaction. To address these challenges, we propose HGACNet, a novel framework that reconstructs complete point clouds of individual objects by hierarchically encoding 3D geometric features and fusing them with image-guided priors from a single-view RGB image. At the core of our approach, the Hierarchical Graph Attention (HGA) encoder adaptively selects critical local points through graph attention-based downsampling and progressively refines hierarchical geometric features to better capture structural continuity and spatial relationships. To strengthen cross-modal interaction, we further design a Multi-Scale Cross-Modal Fusion (MSCF) module that performs attention-based feature alignment between hierarchical geometric features and structured visual representations, enabling fine-grained semantic guidance for completion. In addition, we proposed the contrastive loss (C-Loss) to explicitly align the feature distributions across modalities, improving completion fidelity under modality discrepancy. Finally, extensive experiments conducted on both the ShapeNet-ViPC benchmark and the YCB-Complete dataset confirm the effectiveness of HGACNet, demonstrating state-of-the-art performance as well as strong applicability in real-world robotic manipulation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13692 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HGACNet: Hierarchical Graph Attention Network for Cross-Modal Point Cloud Completion Zeng, Yadan Zhou, Jiadong Li, Xiaohan Chen, I-Ming Robotics Point cloud completion is essential for robotic perception, object reconstruction and supporting downstream tasks like grasp planning, obstacle avoidance, and manipulation. However, incomplete geometry caused by self-occlusion and sensor limitations can significantly degrade downstream reasoning and interaction. To address these challenges, we propose HGACNet, a novel framework that reconstructs complete point clouds of individual objects by hierarchically encoding 3D geometric features and fusing them with image-guided priors from a single-view RGB image. At the core of our approach, the Hierarchical Graph Attention (HGA) encoder adaptively selects critical local points through graph attention-based downsampling and progressively refines hierarchical geometric features to better capture structural continuity and spatial relationships. To strengthen cross-modal interaction, we further design a Multi-Scale Cross-Modal Fusion (MSCF) module that performs attention-based feature alignment between hierarchical geometric features and structured visual representations, enabling fine-grained semantic guidance for completion. In addition, we proposed the contrastive loss (C-Loss) to explicitly align the feature distributions across modalities, improving completion fidelity under modality discrepancy. Finally, extensive experiments conducted on both the ShapeNet-ViPC benchmark and the YCB-Complete dataset confirm the effectiveness of HGACNet, demonstrating state-of-the-art performance as well as strong applicability in real-world robotic manipulation tasks. |
| title | HGACNet: Hierarchical Graph Attention Network for Cross-Modal Point Cloud Completion |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.13692 |