Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization
Authors: Yueming Xu, Haochen Jiang, Zhongyang Xiao, Jianfeng Feng, Li Zhang
NeurIPS 2024 | Venue PDF | 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. |