SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians (2412.10231v1)

Abstract: 3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering. While the vanilla Gaussian Splatting representation is mainly designed for view synthesis, more recent works investigated how to extend it with scene understanding and language features. However, existing methods lack a detailed comprehension of scenes, limiting their ability to segment and interpret complex structures. To this end, We introduce SuperGSeg, a novel approach that fosters cohesive, context-aware scene representation by disentangling segmentation and language field distillation. SuperGSeg first employs neural Gaussians to learn instance and hierarchical segmentation features from multi-view images with the aid of off-the-shelf 2D masks. These features are then leveraged to create a sparse set of what we call Super-Gaussians. Super-Gaussians facilitate the distillation of 2D language features into 3D space. Through Super-Gaussians, our method enables high-dimensional language feature rendering without extreme increases in GPU memory. Extensive experiments demonstrate that SuperGSeg outperforms prior works on both open-vocabulary object localization and semantic segmentation tasks.

• The paper introduces a novel Super-Gaussian mechanism that fuses language features with 3D geometric data for enhanced scene segmentation.
• It leverages pre-trained 2D masks from models like SAM to cluster multi-view features into a sparse and efficient 3D representation.
• Experiments on datasets like ScanNet and LERF-OVS demonstrate its superior performance in handling occlusions and achieving pixel-level semantic precision.

SuperGSeg: Advancements in Open-Vocabulary 3D Segmentation with Structured Super-Gaussians

The paper entitled "SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians" presents an innovative approach to 3D scene segmentation, particularly focusing on achieving nuanced scene understanding through the integration of language features into a 3D Gaussian-based framework. The work aims to address limitations in existing models that struggle with complex scene segmentation and have difficulties integrating detailed language attributes for scene understanding.

Central to the methodology is the novel concept of Super-Gaussians, which extends the traditional 3D Gaussian Splatting (3DGS) approach. SuperGSeg introduces a mechanism where neural Gaussians are employed to learn intricate segmentation features from multi-view images. These features are initially trained using 2D masks from foundation models like SAM, and then clustered into a sparse representation called Super-Gaussians. This new structure facilitates efficient distillation of high-dimensional language features into sparse 3D points without incurring excessive computational costs.

The proposed system outperforms existing open-vocabulary 3D segmentation techniques in multiple contexts, as demonstrated by extensive experiments on datasets such as LERF-OVS and ScanNet. Through SuperGSeg, the integration of language features is significantly enhanced, allowing for minute scene details to be captured and rendered with high fidelity. This is a notable advancement because it links the geometric distribution of Gaussians with language-driven queries, effectively enabling pixel-level semantic segmentation tasks and overcoming occlusion challenges that plagued prior implementations.

The implications of this work are manifold. Practically, it heralds improvements in various fields like robotics, where semantic understanding of the environment is crucial, and in AR/VR applications that require realistic, context-aware scene rendering. Theoretically, this work pushes the boundaries of combining linguistic and visual data, encouraging further exploration of multi-modal integration in AI models.

While the current model makes significant strides, there is room for future research. Further investigation could improve the adaptability of SuperGSeg to dynamically changing environments or diverse datasets. Additionally, enhancing the scalability of the model to accommodate larger scenes with more variable objects while maintaining performance efficiency could be a fruitful avenue. Overall, SuperGSeg presents a robust framework bolstering the capabilities of 3D scene understanding through its structured approach, setting a precedent for subsequent developments in the field.