Camera-Aware Cross-View Alignment for Referring 3D Gaussian Splatting Segmentation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908901650202624 |
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| author | Tao, Yuwen Zhou, Kanglei Tan, Xin Xie, Yuan |
| author_facet | Tao, Yuwen Zhou, Kanglei Tan, Xin Xie, Yuan |
| contents | Referring 3D Gaussian Splatting Segmentation (R3DGS) aims to ground free-form language queries in 3D Gaussian fields. However, existing methods rely on single-view pseudo supervision, leading to viewpoint drift and inconsistent predictions across views. We propose CaRF (Camera-aware Referring Field), a camera-aware cross-view alignment framework for view-consistent referring in 3D Gaussian splatting. CaRF introduces Camera-conditioned Alignment Modulation (CAM) to inject camera geometry into Gaussian-text interactions, and Gaussian-level Cross-view Logit Alignment (GCLA) to explicitly align referring responses of the same Gaussians across calibrated views during training. By turning cross-view discrepancy into an optimizable objective, CaRF enables geometry-aware and view-consistent reasoning directly in the Gaussian space. Extensive experiments on three benchmarks demonstrate that CaRF achieves state-of-the-art performance, improving mIoU by 16.8%, 4.3%, and 2.0% on Ref-LERF, LERF-OVS, and 3D-OVS, respectively. Our code is available at https://github.com/eR3R3/CaRF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Camera-Aware Cross-View Alignment for Referring 3D Gaussian Splatting Segmentation Tao, Yuwen Zhou, Kanglei Tan, Xin Xie, Yuan Computer Vision and Pattern Recognition Referring 3D Gaussian Splatting Segmentation (R3DGS) aims to ground free-form language queries in 3D Gaussian fields. However, existing methods rely on single-view pseudo supervision, leading to viewpoint drift and inconsistent predictions across views. We propose CaRF (Camera-aware Referring Field), a camera-aware cross-view alignment framework for view-consistent referring in 3D Gaussian splatting. CaRF introduces Camera-conditioned Alignment Modulation (CAM) to inject camera geometry into Gaussian-text interactions, and Gaussian-level Cross-view Logit Alignment (GCLA) to explicitly align referring responses of the same Gaussians across calibrated views during training. By turning cross-view discrepancy into an optimizable objective, CaRF enables geometry-aware and view-consistent reasoning directly in the Gaussian space. Extensive experiments on three benchmarks demonstrate that CaRF achieves state-of-the-art performance, improving mIoU by 16.8%, 4.3%, and 2.0% on Ref-LERF, LERF-OVS, and 3D-OVS, respectively. Our code is available at https://github.com/eR3R3/CaRF. |
| title | Camera-Aware Cross-View Alignment for Referring 3D Gaussian Splatting Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.03992 |