3D Vision-Language Gaussian Splatting

• URL: http://arxiv.org/abs/2410.07577v1
• Date: Thu, 10 Oct 2024 03:28:29 GMT
• Title: 3D Vision-Language Gaussian Splatting
• Authors: Qucheng Peng, Benjamin Planche, Zhongpai Gao, Meng Zheng, Anwesa Choudhuri, Terrence Chen, Chen Chen, Ziyan Wu,
• Abstract summary: Multi-modal 3D scene understanding has vital applications in robotics, autonomous driving, and virtual/augmented reality. We propose a solution that achieves adequately handles the distinct visual and semantic modalities. We also employ a camera-view blending technique to improve semantic consistency between existing views.
• Score: 29.047044145499036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in 3D reconstruction methods and vision-language models have propelled the development of multi-modal 3D scene understanding, which has vital applications in robotics, autonomous driving, and virtual/augmented reality. However, current multi-modal scene understanding approaches have naively embedded semantic representations into 3D reconstruction methods without striking a balance between visual and language modalities, which leads to unsatisfying semantic rasterization of translucent or reflective objects, as well as over-fitting on color modality. To alleviate these limitations, we propose a solution that adequately handles the distinct visual and semantic modalities, i.e., a 3D vision-language Gaussian splatting model for scene understanding, to put emphasis on the representation learning of language modality. We propose a novel cross-modal rasterizer, using modality fusion along with a smoothed semantic indicator for enhancing semantic rasterization. We also employ a camera-view blending technique to improve semantic consistency between existing and synthesized views, thereby effectively mitigating over-fitting. Extensive experiments demonstrate that our method achieves state-of-the-art performance in open-vocabulary semantic segmentation, surpassing existing methods by a significant margin.

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