Description: An in-depth exploration of the fusion of Neural Radiance Fields (NeRF) with Simultaneous Localization and Mapping (SLAM) systems. This project introduces the innovative vMAP with Experience Replay (vMAP+ER) method, aiming to enhance 3D modeling through the integration of NeRF within the SLAM framework.
🔑 Key Highlights:
- Innovative Integration: Leveraged NeRF's implicit neural representation capabilities for both scene occupancy and appearance, combined with the robustness of SLAM.
- Methodology: Introduced vMAP+ER, integrating Experience Replay within vMAP for improved learning and mitigation of catastrophic forgetting in SLAM.
- Enhanced Accuracy: Achieved potential improvements in scene reconstruction accuracy, especially in the office-0 scene.
- Computational Challenges: Addressed increased runtime and memory consumption concerns, suggesting areas for optimization.
📊 Results:
- Demonstrated improved reconstruction accuracy in several scenes compared to baseline methods.
- Conducted ablation studies to evaluate buffer size and update mechanisms, revealing subtle performance effects.
- Addressed computational considerations, with increased runtime due to ER and higher memory consumption in dense scenes.
Metric | iMAP | iMAP+ER | vMAP | vMAP+ER | vMAP+ER+bg | vMAP+only bg |
Scene Acc. [cm] ↓ | 2.28 | 2.48 | 3.52 | 3.45 | 3.23 | 3.22 |
Scene Comp. [cm] ↓ | 4.28 | 4.31 | 3.32 | 4.55 | 3.08 | 3.18 |
Scene Comp. Ratio [<1cm %] ↑ | 21.79 | 21.32 | 21.2 | 17.38 | 20.95 | 21.37 |
Scene Comp. Ratio [<5cm %] ↑ | 87.91 | 87.69 | 91.6 | 86.75 | 91.6 | 91.58 |
ㅤ | ㅤ | ㅤ | ㅤ | ㅤ | ㅤ | ㅤ |
Object Acc. [cm] ↓ | - | - | 1.95 | 1.92 | 1.95 | 1.86 |
Object Comp. [cm] ↓ | - | - | 2.44 | 2.56 | 2.53 | 2.34 |
Object Comp. Ratio [<5cm %] ↑ | - | - | 91.41 | 91.04 | 90.44 | 91.63 |
Object Comp. Ratio [<1cm %] ↑ | - | - | 61.67 | 60.73 | 61.06 | 63.56 |
- The average training time when using a replay buffer for both the object and the background (vMAP+ER+BG) is approximately 54.72% longer per iteration than that of vMAP.
- When using a replay buffer only for the background (vMAP+BG), the increase is negligible.
🔚 Conclusion:
The integration of Neural Radiance Fields (NeRF) and Experience Replay (ER) within the vMAP framework presents a potential direction in SLAM and 3D reconstruction. The proposed vMAP+ER method demonstrates potential improvements in reconstruction accuracy while introducing new computational considerations in terms of runtime and memory. Future work can focus on optimizing the computational efficiency of ER integration, possibly through more careful buffer selection. Additionally, the proposed method can be extended to other SLAM frameworks, such as ORB-SLAM, to further improve the reconstruction accuracy and completeness.