PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM (2501.00352v1)

Abstract: Understanding geometric, semantic, and instance information in 3D scenes from sequential video data is essential for applications in robotics and augmented reality. However, existing Simultaneous Localization and Mapping (SLAM) methods generally focus on either geometric or semantic reconstruction. In this paper, we introduce PanoSLAM, the first SLAM system to integrate geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation within a unified framework. Our approach builds upon 3D Gaussian Splatting, modified with several critical components to enable efficient rendering of depth, color, semantic, and instance information from arbitrary viewpoints. To achieve panoptic 3D scene reconstruction from sequential RGB-D videos, we propose an online Spatial-Temporal Lifting (STL) module that transfers 2D panoptic predictions from vision models into 3D Gaussian representations. This STL module addresses the challenges of label noise and inconsistencies in 2D predictions by refining the pseudo labels across multi-view inputs, creating a coherent 3D representation that enhances segmentation accuracy. Our experiments show that PanoSLAM outperforms recent semantic SLAM methods in both mapping and tracking accuracy. For the first time, it achieves panoptic 3D reconstruction of open-world environments directly from the RGB-D video. (https://github.com/runnanchen/PanoSLAM)

• The paper introduces PanoSLAM, a novel SLAM system unifying geometric, 3D semantic, and 3D instance reconstruction using 3D Gaussian Splatting and online label refinement.
• PanoSLAM leverages vision foundation models for zero-shot perception and employs a Spatial-Temporal Lifting module to achieve label-free panoptic 3D reconstruction in open-world environments.
• Evaluations show PanoSLAM outperforms state-of-the-art semantic SLAM methods in mapping and tracking, offering significant practical implications for robotics, AR, and autonomous driving.

PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM

The authors introduce PanoSLAM, a novel SLAM system that unifies geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation. This unified framework is both an innovation and a solution to the limitations of existing SLAM systems, which typically focus on either geometric or semantic reconstruction but not both.

Technical Approach

PanoSLAM is based on 3D Gaussian Splatting, an efficient method for scene representation and rendering. The system leverages an online Spatial-Temporal Lifting (STL) module to address label noise and inconsistencies in 2D panoptic predictions from vision models. This STL module refines pseudo-labels across multi-view inputs to create a coherent 3D representation, which significantly improves segmentation accuracy. This is vital since manual labeling of scenes in open-world environments is both complex and costly.

The system's ability to transform 2D panoptic predictions into 3D representations without manual annotations marks a significant advancement. By integrating the vision foundation models such as CLIP and SAM for zero-shot perception, PanoSLAM extends the boundaries of traditional semantic SLAM. This adaptation overcomes a critical challenge in the field: the requirement for extensive offline optimization.

Experimental Validation

PanoSLAM was evaluated using benchmark datasets Replica and ScanNet++, showing superior performance in mapping and tracking accuracy over recent state-of-the-art semantic SLAM methods. The authors report that PanoSLAM is the first framework to achieve panoptic 3D reconstruction of open-world environments from RGB-D video, without manual labels. These results underscore the efficacy and robustness of the proposed approach.

Implications and Future Directions

Practically, PanoSLAM can enhance applications in robotics, augmented reality, and autonomous driving by providing a comprehensive understanding of environments with minimal manual intervention. Theoretically, this work contributes to bridging the gap between geometric and semantic SLAM, offering a new paradigm for fully autonomous scene understanding systems.

Looking forward, the further integration of multi-modal sensory information could refine the labels' accuracy, enhancing PanoSLAM's semantic reconstruction capability. Additionally, optimizing the model for real-time processing could make it more applicable to dynamic environments.

In summary, PanoSLAM not only demonstrates a significant leap in SLAM technology by achieving label-free panoptic 3D scene reconstruction but also sets a promising foundation for future research in autonomous 3D scene understanding.