SAI3D: Segment Any Instance in 3D Scenes (2312.11557v2)

Abstract: Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets, limiting their application to a narrow spectrum of object categories. Recent efforts have sought to harness vision-LLMs like CLIP for open-set semantic reasoning, yet these methods struggle to distinguish between objects of the same categories and rely on specific prompts that are not universally applicable. In this paper, we introduce SAI3D, a novel zero-shot 3D instance segmentation approach that synergistically leverages geometric priors and semantic cues derived from Segment Anything Model (SAM). Our method partitions a 3D scene into geometric primitives, which are then progressively merged into 3D instance segmentations that are consistent with the multi-view SAM masks. Moreover, we design a hierarchical region-growing algorithm with a dynamic thresholding mechanism, which largely improves the robustness of finegrained 3D scene parsing.Empirical evaluations on ScanNet, Matterport3D and the more challenging ScanNet++ datasets demonstrate the superiority of our approach. Notably, SAI3D outperforms existing open-vocabulary baselines and even surpasses fully-supervised methods in class-agnostic segmentation on ScanNet++. Our project page is at https://yd-yin.github.io/SAI3D.

Overview of SAI3D: Segment Any Instance in 3D Scenes

The paper presents SAI3D, a method for zero-shot 3D instance segmentation that integrates geometric and semantic cues to segment 3D scenes without relying on 3D annotations. The importance of SAI3D is highlighted by its ability to overcome the limitations of previous 3D segmentation methods which heavily rely on annotated datasets and are therefore restricted in their applicability across diverse object categories. This approach capitalizes on the Segment Anything Model (SAM) for 2D instance segmentation and introduces a hierarchical algorithm that grows regions dynamically, enhancing the robustness of 3D segmentation.

Key Contributions

1. Zero-Shot Learning: SAI3D is designed for zero-shot segmentation, meaning it does not require training on specific datasets, thereby facilitating application in open-ended scenarios and allowing generalization to new objects and environments.
2. Segmentation Approach: The method involves partitioning the 3D scene into geometric primitives, which are progressively merged based on affinities derived from SAM-generated masks across multiple views. This process ensures that the final 3D segmentation captures both geometric and semantic consistency.
3. Hierarchical Region-Growing Algorithm: The introduction of a dynamic thresholding mechanism within this algorithm greatly improves the precision and adaptability of the segmentation process, particularly in fine-grained scene parsing.
4. Comparative Performance: Empirical results on datasets such as ScanNet, Matterport3D, and ScanNet++ indicate that SAI3D outperforms existing open-vocabulary and even fully-supervised class-agnostic segmentation methods, notably on ScanNet++. For instance, on ScanNet++, an AP$_{50}$ score of 31.1 was achieved by SAI3D, surpassing SAM3D’s score of 14.2, showing its effectiveness in complex scenes.

Implications

The emergence of SAI3D marks a significant shift towards more adaptable and efficient methods in the domain of 3D instance segmentation. Its zero-shot capabilities suggest potential avenues for further exploration in AI models that operate without the constraints of labeled data, which may lead to advancements in applications like robotics and autonomous navigation where new object categories are constantly encountered.

Furthermore, the ability to provide high-quality 3D segmentations without the need for exhaustive annotations opens up possibilities for unsupervised learning paradigms, where systems can learn and adapt directly from their interactions with their environments. The robustness of this segmentation approach across different datasets also encourages its use in diverse real-world settings, potentially accelerating the development of AI systems that require spatial awareness and context understanding.

Future Directions

Focusing on optimizing the integration of foundational models like SAM with real-world data and examining how these models can be refined or expanded will likely be pivotal for further breakthroughs in the field. Identifying strategies that enhance the computational efficiency of such methods will also be crucial, particularly for scaling them to handle large or complex scenes effectively in practical applications.

As the field progresses, experimenting with combinations of advanced neural networks and fewer samples could lead to more generalized models that improve upon the challenges of 3D segmentation, particularly those related to occlusion and varying point densities. Such improvements could further impact the theoretical understanding of instance segmentation, allowing for better integrations between AI semantic reasoning and geometric scene comprehension.