DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization
Authors: Yueming Xu, Haochen Jiang, Zhongyang Xiao, Jianfeng Feng, Li Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that DG-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, and novel-view synthesis in dynamic scenes, outperforming existing methods meanwhile preserving real-time rendering ability. |
| Researcher Affiliation | Collaboration | Yueming Xu1 Haochen Jiang1 Zhongyang Xiao2 Jianfeng Feng1 Li Zhang1 1Fudan University 2Autonomous Driving Division, NIO |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://github.com/fudan-zvg/DG-SLAM |
| Open Datasets | Yes | Our methodology is evaluated using three publicly available challenging datasets: TUM RGB-D dataset [38], BONN RGB-D Dynamic dataset [5] and Scan Net [39]. |
| Dataset Splits | No | The paper discusses training and optimization on datasets but does not explicitly provide details about separate training, validation, and test splits with percentages or sample counts. |
| Hardware Specification | Yes | We run our DG-SLAM on an RTX 3090 Ti GPU at 2 FPS on BONN datasets, which takes roughly 9GB of memory. |
| Software Dependencies | No | We utilize Oneformer [41] to generate prior semantic segmentation. For the depth wrap mask, we set the window size to 4 and the depth threshold to 0.6. |
| Experiment Setup | Yes | We set the loss weight λ1 = 0.9 , λ2 = 0.2 and λ3 = 0.1 to train our model. The number of iterations for the tracking and mapping processes has been set to 20 and 40, respectively. For the Gaussian points deleting, we set τα = 0.005, τS1 = 0.4 and τS2 = 36 to avoid the generation of abnormal Gaussian points. What s more, we utilize Oneformer [41] to generate prior semantic segmentation. For the depth wrap mask, we set the window size to 4 and the depth threshold to 0.6. We also adopt the keyframe selection strategy from DROID-VO [19] based on optical flow. |