Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding (2311.18482v1)

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.

• The paper presents a new framework that embeds compact language features into 3D Gaussians to enable open-vocabulary scene querying.
• It employs an innovative quantization scheme to reduce memory usage while preserving robust semantic information in complex 3D environments.
• The model achieves real-time, high-quality rendering and precise language-based queries, enhancing applications in VR, autonomous vehicles, and robotics.

Advancements in AI have been marching steadfastly towards creating more intuitive methods for human-machine interaction, particularly in understanding and interpreting complex visual environments. A recent breakthrough in this field is a novel scene representation framework called Language Embedded 3D Gaussians, which significantly enhances open-vocabulary querying in 3D spaces—essentially enabling users to seek out and identify objects within a 3D scene using natural language queries.

Bridging Language and 3D Scene Understanding

Open-vocabulary querying is the ability to ask an AI system about anything in its field of view without being limited by a predefined set of query terms. Until now, this was a challenging endeavor due to the computationally expensive nature of the models that needed to process and understand both linguistic and visual input.

The new language-embedded scene representation builds on techniques like Neural Radiance Fields (NeRFs) for high-quality view synthesis and 3D Gaussian Splatting for fast rendering. While these have excelled at creating photorealistic 3D scenes from 2D images, they lacked the semantic understanding to be queried with natural language. Current methods managed this by incorporating dense language features directly into the scene representation, but this approach was resource-intensive and limited in performance.

A Novel Approach to Semantic Scene Understanding

The recent method avoids these pitfalls by introducing a dedicated quantization scheme that reduces the memory footprint and computing costs without sacrificing the robustness of scene semantics. This quantization exploits redundancy in language features across the 3D space, condensing the high-dimensional semantic data extracted from images into a compact representation.

An inventive element of this solution is the embedding of these compact language features onto 3D Gaussians, which are used to represent the scene at hand. To handle semantic ambiguities arising from varied viewpoints, an adaptive learning mechanism that determines the certainty of visual features is used. This system smoothes out the semantic embedding across the scene intuitively, maintaining precision in the language queries.

Real-time, High-Quality Visualization with Efficient Resource Management

What sets this model apart is its achievement in rendering high-fidelity novel views and its accuracy in language-based querying tasks, while being incredibly efficient in memory usage and rendering speed. It significantly outperforms existing language-embedded 3D scene representations on all fronts, allowing for real-time interactions on regular consumer hardware.

Applications Galore with Open-Vocabulary Scene Understanding

The implications of such technology are broad and impactful. For instance, by leveraging LLMs for communication, users could "speak" to virtual and augmented reality environments to find objects or navigate scenes. Autonomous vehicles may benefit from this by recognizing and understanding unanticipated obstacles or objects. It also opens up potential for content creation, gaming, and robotics, where users could manipulate and interact with 3D spaces in a more natural, human-like manner.

In conclusion, Language Embedded 3D Gaussians represent a significant step toward merging the world of language with 3D space understanding, making it more accessible, efficient, and practical for real-world applications. This advancement not only brings us closer to more sophisticated human-AI interaction but also paves the way for a future where technology can seamlessly integrate into our lives with understanding that borders on intuitive.