OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning (2311.11666v1)

Abstract: Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework, which is accomplished by two steps. Firstly, we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly, image features rendered from the 3D feature field are clustered at different levels, which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations, this framework yields a global consistent 3D feature field, which further enables hierarchical segmentation, multi-object selection, and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation.

• The paper presents a novel hierarchical contrastive learning framework that unifies multi-view 2D segmentations into a coherent 3D feature field.
• It achieves superior segmentation accuracy with significantly higher mIoU compared to existing methods on datasets such as Replica.
• The framework enables interactive, class-agnostic 3D segmentation with wide applications in robotics, virtual reality, and medical imaging.

Overview of "OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning"

This paper introduces OmniSeg3D, a comprehensive 3D segmentation framework utilizing hierarchical contrastive learning. By attempting to merge multi-view, inconsistent, class-agnostic 2D segmentations into a uniform 3D feature field, this method targets the challenge of segmenting a range of complex 3D scenes without pre-defined category constraints. It also aims to enhance the understanding of hierarchical structure within these scenes. This model supports hierarchical segmentation at multiple levels, multi-object selection, and complete scene discretization, facilitated by an interactive user interface.

Methodology

The core methodology of OmniSeg3D lies in its novel hierarchical contrastive learning framework, which serves two main components:

1. Hierarchical Representation: Class-agnostic 2D segmentation techniques are employed to generate masks indicating various object components. These masks are then structured into a hierarchical representation, preserving part-whole relationships vital for 3D understanding.
2. 3D Feature Field Learning: Through contrastive learning, inconsistent 2D features extracted from images rendered via a 3D NeRF-based model are clustered. A regularization mechanism favoring hierarchical relationships is applied to bring related features closer while pushing unrelated ones apart, creating a globally consistent 3D feature field that is capable of accurate segmentation tasks.

Experimental Validation

To validate the effectiveness of the proposed method, extensive experiments were conducted across various datasets, including the Replica dataset for hierarchical segmentation. The results highlight OmniSeg3D’s superior capability in achieving accurate 3D segmentation with an average mIoU significantly higher than existing methods. The framework's powerful adaptability is showcased, particularly in effectively handling unseen classes and segmenting scenes with multiple hierarchical levels.

Implications and Future Work

OmniSeg3D embarks on the path towards category-agnostic 3D segmentation, offering a framework that promises improved interaction with 3D data, with potential applications in robotics, virtual reality, and detailed 3D visualization. The hierarchical structuring also hints at the possibility of differentiated task applications within the same dataset, such as discerning architectural features from entire buildings in urban modeling or from anatomical features in medical imaging.

Future development could delve into higher-dimensional feature fields or integrate semantic transcription from textual input to further fine-tune the hierarchical understanding and segmentation results. Further exploration into mitigating the reliance on exact view or angle dependencies within the input data sets may unlock broader generalization capabilities for real-world applications.

Conclusion

OmniSeg3D introduces a significant step in advancing 3D segmentation technologies by providing a robust, hierarchical-centric method that radically departs from bounded class-driven models. By fostering a deeper comprehension of spatial hierarchies within complex structures, this framework stands as a pivotal building block for enriching AI-driven visual understanding in three dimensions.