Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding

• URL: http://arxiv.org/abs/2311.18482v1
• Date: Thu, 30 Nov 2023 11:50:07 GMT
• Title: Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
• Authors: Jin-Chuan Shi, Miao Wang, Hao-Bin Duan, Shao-Hua Guan
• Abstract summary: We introduce Language Embedded 3D Gaussians, a novel scene representation for open-vocabulary query tasks. Our representation achieves the best visual quality and language querying accuracy across current language-embedded representations.
• Score: 2.517953665531978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-vocabulary querying in 3D space is challenging but essential for scene understanding tasks such as object localization and segmentation. Language-embedded scene representations have made progress by incorporating language features into 3D spaces. However, their efficacy heavily depends on neural networks that are resource-intensive in training and rendering. Although recent 3D Gaussians offer efficient and high-quality novel view synthesis, directly embedding language features in them leads to prohibitive memory usage and decreased performance. In this work, we introduce Language Embedded 3D Gaussians, a novel scene representation for open-vocabulary query tasks. Instead of embedding high-dimensional raw semantic features on 3D Gaussians, we propose a dedicated quantization scheme that drastically alleviates the memory requirement, and a novel embedding procedure that achieves smoother yet high accuracy query, countering the multi-view feature inconsistencies and the high-frequency inductive bias in point-based representations. Our comprehensive experiments show that our representation achieves the best visual quality and language querying accuracy across current language-embedded representations, while maintaining real-time rendering frame rates on a single desktop GPU.

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