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..
DENSE RGB SLAM WITH NEURAL IMPLICIT MAPS
Authors: Heng Li, Xiaodong Gu, Weihao Yuan, luwei yang, Zilong Dong, Ping Tan
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM systems. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods. |
| Researcher Affiliation | Collaboration | 1Hong Kong University of Science and Technology, 2Alibaba Group, 3Simon Fraser University |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It describes the pipeline textually and with a diagram. |
| Open Source Code | Yes | The code is at poptree.github.io/DIM-SLAM/. |
| Open Datasets | Yes | We evaluate our method on three widely used datasets: TUM RGB-D (Sturm et al., 2012a), Eu Ro C (Burri et al., 2016), and Replica (Straub et al., 2019) dataset. |
| Dataset Splits | No | The paper mentions using datasets for evaluation and provides details on sampling points for rendering and optimization windowing, but it does not specify explicit training/validation/test dataset splits with percentages, sample counts, or predefined split references. |
| Hardware Specification | Yes | Our method is implemented with Py Torch (Paszke et al., 2017) and runs on a server with two NVIDIA 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions that the method is implemented with PyTorch but does not provide specific version numbers for PyTorch or any other software dependencies required for reproducibility. |
| Experiment Setup | Yes | We fix the iteration number of initialization Ni to 1500, the number of sampling pixels |M| to 3000, the size of the window |W| to 21. The iteration number of window optimization for ours(one thread) Nw is 100. In our two-thread version, we change Nw to 20 for tracking. We use the Adam (Kingma & Ba, 2014) optimizer to optimize both the implicit map representation and the camera poses, with learning rates of 0.001 and 0.01, respectively. The αwarping, αrender, αsmooth is 0.5, 0.1, 0.01, respectively. |