3D Vision-Language Gaussian Splatting

Published: 22 Jan 2025, Last Modified: 18 Feb 2025ICLR 2025 PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Multi-modal 3D Scene Understanding, Gaussian Splatting, Open-vocabulary Semantic Segmentation, Open-vocabulary Object Localization
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.

Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 4211
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