Click-Gaussian: Interactive Segmentation to Any 3D Gaussians

Click-Gaussian: Interactive Segmentation to Any 3D Gaussians

The paper "Click-Gaussian: Interactive Segmentation to Any 3D Gaussians" presents an effective method for interactive segmentation of 3D Gaussians, leveraging the advancements in 3D Gaussian Splatting (3DGS) and neural rendering technologies. The proposed method, Click-Gaussian, addresses key limitations in existing segmentation techniques, such as time-consuming post-processing and inconsistent segmentation across views.

Interactive segmentation within 3D environments presents unique challenges due to the complex nature of 3D scene representation and the necessity for fine-grained manipulation. Traditional methods often rely on 2D segmentation results independently obtained from multiple views, leading to inconsistencies and noise in 3D segmentation. This paper proposes using 3D Gaussians with two-level granularity feature fields derived from 2D segmentation masks, facilitating detailed segmentation without extensive post-processing.

Key Contributions

Click-Gaussian introduces a novel approach that augments 3D Gaussians with distinctive feature fields at two granular levels—coarse and fine. This segmentation method is powered by contrastive learning and a granularity prior, which utilizes two-level masks to train the semantic features of 3D Gaussians. A significant innovation in this paper is the Global Feature-guided Learning (GFL) strategy, which consistently informs the training process by clustering feature candidates from noisy 2D segments across views.

The paper highlights several critical contributions:

1. Two-Level Granularity Feature Fields: By augmenting 3D Gaussians with feature fields at two granular levels, Click-Gaussian captures coarse and fine details within a scene, optimizing feature learning by leveraging a granularity prior.
2. Global Feature-guided Learning (GFL): GFL effectively addresses the challenge of inconsistently learned features across multiple views by smoothening noises and ensuring reliable training signals, significantly enhancing segmentation accuracy.
3. Efficiency and Accuracy: Click-Gaussian achieves real-time interactive segmentation, running at 10 ms per click, which is 15 to 130 times faster than previous methods. This efficiency is coupled with a substantial improvement in segmentation accuracy, as evidenced by extensive experiments on real-world scenes.

Methods and Techniques

The methodological foundation of Click-Gaussian involves several key techniques:

• Contrastive Learning: This technique ensures that feature fields are distinctive by maximizing the cosine similarity between features of the same segment and minimizing it for different segments.
• Granularity Prior: By splitting feature vectors into coarse and fine components, the method leverages intrinsic dependencies between granular levels to improve fine-level feature learning.
• Regularization Techniques: Multiple regularization strategies, including hypersphere regularization and spatial consistency, are employed to stabilize feature learning and enhance segmentation robustness.

Experimental Validation

The efficacy of Click-Gaussian is validated through experiments on two public datasets: LERF-Mask and SPIn-NeRF datasets. The results demonstrate significant advancements in both coarse and fine-level segmentation compared to several baseline methods such as Gau-Group, OmniSeg3D, Feature3DGS, and GARField. Click-Gaussian not only achieves higher mIoU scores but also excels in terms of computational efficiency and segmentation precision.

The experiments include evaluations of multi-view segmentation tasks and 3D Gaussian extraction tasks, showcasing the practical applications of Click-Gaussian in facilitating detailed and accurate scene modifications. The method's robust performance across various real-world scenes underscores its potential for widespread application in virtual and augmented reality, digital content creation, and real-time interactive systems.

Implications and Future Developments

The implications of this research are profound for the field of 3D scene manipulation and neural rendering. Click-Gaussian's approach to leveraging two-level granularity and GFL can influence future developments in interactive segmentation technologies. Moreover, the method's ability to provide real-time, fine-grained segmentation without extensive post-processing opens up new possibilities for user-driven modification of 3D environments.

Future research could explore extending the granularity concept to more than two levels for even finer segmentation details. Additionally, integrating Click-Gaussian with advanced diffusion models for content generation could enhance both the fidelity and diversity of 3D scene synthesis. The continued evolution of this method promises to contribute significantly to the fields of interactive graphics and virtual environment manipulation.

In summary, the paper presents a well-founded approach to overcoming the challenging aspect of interactive 3D segmentation. Click-Gaussian's blend of efficiency, accuracy, and real-time capability represents a notable advancement, paving the way for more intuitive and responsive 3D object manipulation in diverse applications.