CaRF: Enhancing Multi-View Consistency in Referring 3D Gaussian Splatting Segmentation

• URL: http://arxiv.org/abs/2511.03992v1
• Date: Thu, 06 Nov 2025 02:24:04 GMT
• Title: CaRF: Enhancing Multi-View Consistency in Referring 3D Gaussian Splatting Segmentation
• Authors: Yuwen Tao, Kanglei Zhou, Xin Tan, Yuan Xie,
• Abstract summary: Referring 3D Gaussian Splatting (R3DGS) aims to interpret free-form language expressions and localize the corresponding 3D regions in Gaussian fields.<n>Existing pipelines still struggle with cross-modal alignment between language and 3D geometry.<n>We present Camera Aware Referring Field (CaRF), a framework that operates directly in the 3D Gaussian space and achieves multi view consistency.<n>This work promotes more reliable and view consistent 3D scene understanding, with potential benefits for embodied AI, AR/VR interaction, and autonomous perception.
• Score: 20.561664000265765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring 3D Gaussian Splatting Segmentation (R3DGS) aims to interpret free-form language expressions and localize the corresponding 3D regions in Gaussian fields. While recent advances have introduced cross-modal alignment between language and 3D geometry, existing pipelines still struggle with cross-view consistency due to their reliance on 2D rendered pseudo supervision and view specific feature learning. In this work, we present Camera Aware Referring Field (CaRF), a fully differentiable framework that operates directly in the 3D Gaussian space and achieves multi view consistency. Specifically, CaRF introduces Gaussian Field Camera Encoding (GFCE), which incorporates camera geometry into Gaussian text interactions to explicitly model view dependent variations and enhance geometric reasoning. Building on this, In Training Paired View Supervision (ITPVS) is proposed to align per Gaussian logits across calibrated views during training, effectively mitigating single view overfitting and exposing inter view discrepancies for optimization. Extensive experiments on three representative benchmarks demonstrate that CaRF achieves average improvements of 16.8%, 4.3%, and 2.0% in mIoU over state of the art methods on the Ref LERF, LERF OVS, and 3D OVS datasets, respectively. Moreover, this work promotes more reliable and view consistent 3D scene understanding, with potential benefits for embodied AI, AR/VR interaction, and autonomous perception.

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