DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910170864418816 |
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| author | Zhang, Yani Wu, Dongming Shi, Hao Liu, Yingfei Wang, Tiancai Dong, Xingping |
| author_facet | Zhang, Yani Wu, Dongming Shi, Hao Liu, Yingfei Wang, Tiancai Dong, Xingping |
| contents | A core task in embodied intelligence is ego-centric 3D visual grounding. Existing methods typically adopt two-stage, heterogeneous pipelines that pair a detector with a separate grounding model. Incompatible decoders and box heads hinder the transfer of object-level priors, and the split training causes redundant re-optimization. To overcome these limitations, we present DEGround, a straight, elegant, and effective framework that centers on object-level sharing over detection and grounding. It employs a set of queries that serves as the common object representation for both detection and grounding, which is decoded by a shared transformer and bounding box head. Building on this homogeneous framework, we further introduce two task-specific plug-in modules to enhance fine-grained instruction grounding. The Regional Activation Grounding module improves spatial-textual alignment by highlighting instruction-relevant regions, while the Query-wise Modulation module applies sentence-conditioned affine modulation to generate instruction-aware queries at initialization. Extensive experiments demonstrate that DEGround achieves the best performance on multiple benchmarks. Remarkably, it significantly outperforms previous methods by 7.52% at overall precision on the EmbodiedScan dataset. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_05199 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework Zhang, Yani Wu, Dongming Shi, Hao Liu, Yingfei Wang, Tiancai Dong, Xingping Computer Vision and Pattern Recognition A core task in embodied intelligence is ego-centric 3D visual grounding. Existing methods typically adopt two-stage, heterogeneous pipelines that pair a detector with a separate grounding model. Incompatible decoders and box heads hinder the transfer of object-level priors, and the split training causes redundant re-optimization. To overcome these limitations, we present DEGround, a straight, elegant, and effective framework that centers on object-level sharing over detection and grounding. It employs a set of queries that serves as the common object representation for both detection and grounding, which is decoded by a shared transformer and bounding box head. Building on this homogeneous framework, we further introduce two task-specific plug-in modules to enhance fine-grained instruction grounding. The Regional Activation Grounding module improves spatial-textual alignment by highlighting instruction-relevant regions, while the Query-wise Modulation module applies sentence-conditioned affine modulation to generate instruction-aware queries at initialization. Extensive experiments demonstrate that DEGround achieves the best performance on multiple benchmarks. Remarkably, it significantly outperforms previous methods by 7.52% at overall precision on the EmbodiedScan dataset. |
| title | DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05199 |