Online Language Splatting (2503.09447v1)

Abstract: To enable AI agents to interact seamlessly with both humans and 3D environments, they must not only perceive the 3D world accurately but also align human language with 3D spatial representations. While prior work has made significant progress by integrating language features into geometrically detailed 3D scene representations using 3D Gaussian Splatting (GS), these approaches rely on computationally intensive offline preprocessing of language features for each input image, limiting adaptability to new environments. In this work, we introduce Online Language Splatting, the first framework to achieve online, near real-time, open-vocabulary language mapping within a 3DGS-SLAM system without requiring pre-generated language features. The key challenge lies in efficiently fusing high-dimensional language features into 3D representations while balancing the computation speed, memory usage, rendering quality and open-vocabulary capability. To this end, we innovatively design: (1) a high-resolution CLIP embedding module capable of generating detailed language feature maps in 18ms per frame, (2) a two-stage online auto-encoder that compresses 768-dimensional CLIP features to 15 dimensions while preserving open-vocabulary capabilities, and (3) a color-language disentangled optimization approach to improve rendering quality. Experimental results show that our online method not only surpasses the state-of-the-art offline methods in accuracy but also achieves more than 40x efficiency boost, demonstrating the potential for dynamic and interactive AI applications.

• The paper introduces Online Language Splatting, a novel framework integrating real-time language mapping into 3D Gaussian Splatting within SLAM systems.
• Key methodological innovations include a real-time high-resolution CLIP embedding module using a Super-Resolution Decoder and a two-stage feature compression autoencoder.
• Experimental results demonstrate the system achieves over 40x runtime efficiency improvement and surpasses offline methods in accuracy, enabling practical applications in robotics and AR.

Overview of "Online Language Splatting"

The paper "Online Language Splatting" represents a significant advancement in the integration of language features into 3D scene representations within real-time applications. The authors introduce an innovative framework that bridges the gap between textual data and spatial understanding, thus enabling AI systems to interpret and interact with 3D environments through language commands effectively.

The core contribution lies in the development of an online, near real-time language mapping system within a 3D Gaussian Splatting (3DGS) framework, functioning seamlessly as part of a SLAM system. This framework, termed Online Language Splatting, addresses several computational and operational challenges previously limiting the adaptability of static, offline language mapping methods. Unlike offline systems that rely on preprocessed language data, the proposed system operates dynamically, producing high-resolution language feature maps on-the-fly without pre-generated language features, thereby preserving open-vocabulary capabilities.

Methodological Innovations

The authors introduce several key components to resolve the challenges associated with integrating language features into 3D representations while ensuring efficiency and quality:

1. Real-time High-Resolution CLIP Embedding: The paper presents a novel high-resolution CLIP embedding module, which accelerates the generation of detailed language feature maps to an impressive 18 milliseconds per frame. This capability is achieved through a Super-Resolution Decoder (SRD) that refines coarse CLIP embeddings without the heavy computation typically associated with such tasks.
2. Two-Stage Feature Compression: Efficient feature compression is critical for managing high-dimensional language data. The authors implement a two-stage autoencoder that compresses the CLIP feature space from 768 dimensions down to 15, thereby enabling fast processing while retaining essential open-vocabulary characteristics. This process involves both a pre-trained feature compression stage and a scene-adaptive online autoencoder, ensuring robust feature encoding across diverse environments.
3. Color-Language Disentangled Optimization: Recognizing the distinct rendering requirements of language and color data, the authors propose a disentangled optimization approach. This methodology segregates the parameters governing Gaussian scale and rotation for language features from those for color features, thereby optimizing rendering results for both modalities more effectively.

Experimental Results

The authors validate the system's performance through extensive experiments on datasets such as Replica and TUM RGB-D. Their method surpasses the accuracy of state-of-the-art offline language mapping methods while achieving more than a 40-fold improvement in runtime efficiency. In terms of integration into existing SLAM frameworks, the system maintains rendering quality and camera localization performance, thus demonstrating its practical applicability without compromising the foundational SLAM functions.

Implications and Future Directions

This research not only pushes the boundary of integrating language processing into 3D spatial systems but also broadens the scope for practical AI applications requiring real-time interaction with environments. The ability to process language inputs dynamically opens avenues for advancements in robotics, augmented reality, and interactive virtual environments.

Future developments could focus on expanding the system's capabilities to handle more complex, dynamic scenes with moving objects or varying lighting conditions. Additionally, further exploring the integration with larger, multi-modal AI systems would be beneficial in enhancing the robustness and breadth of applications, enabling a more seamless interaction between language input and 3D system outputs.

Overall, "Online Language Splatting" demonstrates an effective convergence of natural language processing and 3D computer vision, reflecting a significant step toward the next generation of highly interactive and adaptable AI systems.