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LangSplat: 3D Language Gaussian Splatting (2312.16084v2)

Published 26 Dec 2023 in cs.CV

Abstract: Humans live in a 3D world and commonly use natural language to interact with a 3D scene. Modeling a 3D language field to support open-ended language queries in 3D has gained increasing attention recently. This paper introduces LangSplat, which constructs a 3D language field that enables precise and efficient open-vocabulary querying within 3D spaces. Unlike existing methods that ground CLIP language embeddings in a NeRF model, LangSplat advances the field by utilizing a collection of 3D Gaussians, each encoding language features distilled from CLIP, to represent the language field. By employing a tile-based splatting technique for rendering language features, we circumvent the costly rendering process inherent in NeRF. Instead of directly learning CLIP embeddings, LangSplat first trains a scene-wise language autoencoder and then learns language features on the scene-specific latent space, thereby alleviating substantial memory demands imposed by explicit modeling. Existing methods struggle with imprecise and vague 3D language fields, which fail to discern clear boundaries between objects. We delve into this issue and propose to learn hierarchical semantics using SAM, thereby eliminating the need for extensively querying the language field across various scales and the regularization of DINO features. Extensive experimental results show that LangSplat significantly outperforms the previous state-of-the-art method LERF by a large margin. Notably, LangSplat is extremely efficient, achieving a 199 $\times$ speedup compared to LERF at the resolution of 1440 $\times$ 1080. We strongly recommend readers to check out our video results at https://langsplat.github.io/

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Citations (93)
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Summary

  • The paper introduces LangSplat, a novel method that fuses 3D Gaussian features with CLIP embeddings to create precise 3D language fields.
  • The paper leverages the Segment Anything Model and a scene-specific autoencoder to significantly reduce memory usage and achieve up to 199x speed improvements over LERF.
  • The paper demonstrates superior performance in tasks like open-vocabulary object localization and semantic segmentation, attaining an 84.3% accuracy on benchmark datasets.

Understanding 3D Language Fields via LangSplat

Introduction to 3D Language Fields

The interaction between humans and three-dimensional (3D) environments often involves natural language, which can serve as a powerful tool for querying and understanding complex scenes. Recently, there has been notable interest in developing techniques that enable computers to interpret open-ended language queries within 3D contexts. A potent approach to this challenge lies in 3D language fields, where the goal is to model the relationship between 3D points in a space and the natural language descriptions they entail.

The LangSplat Methodology

Previous methods have struggled to capture crisp boundaries and create precise 3D language features, which are essential for distinguishing individual objects within a scene. To overcome these limitations, a novel method named LangSplat has been introduced. LangSplat advances over predecessors like LERF (Language Embedded Radiance Fields) by proposing a blend of 3D Gaussian features and language embeddings derived from the CLIP model. This blend is referred to as 3D language Gaussians.

A key innovation in LangSplat is the implementation of the Segment Anything Model (SAM). SAM enables the extraction of CLIP features with high precision, directly influencing each point in segmented regions and hence, significantly enhancing 3D language field accuracy. Moreover, LangSplat introduces a scene-specific autoencoder that alleviates the extensive memory demands generally required by such explicit modeling. By learning latent language spaces specific to each scene, LangSplat significantly reduces memory usage while maintaining feature richness.

Efficiency and Accuracy

LangSplat impressively outperforms LERF in efficiency, boasting up to a 199x speed increase at high resolutions in benchmarks. It manages to maintain this efficiency without compromising on accuracy. Instead, it demonstrates superior performance in tasks like open-vocabulary 3D object localization and semantic segmentation. For instance, in the LERF dataset, LangSplat achieves an 84.3% accuracy rate, which is a substantial improvement over the former state-of-the-art methods.

Real-world Applications

The implications of such advancements are profound for several domains such as robotics, autonomous driving, and augmented reality. Robots, for example, can better understand and navigate their environment when equipped with the capability to process and respond to natural language instructions. Similarly, enhanced interpretative abilities can improve safety features in autonomous vehicles, and provide more immersive experiences in virtual environments.

Conclusion

LangSplat marks a significant leap in 3D scene understanding driven by natural language processing. Its combination of speed, efficiency, and precision lays the groundwork for more advanced systems capable of intuiting complex queries within 3D spaces. As techniques like LangSplat continue to evolve, so too does the potential for creating more natural and intuitive human-computer interfaces, further closing the gap between our physical reality and the field of digital comprehension.

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