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..
DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors
Authors: Xiaze Zhang, Ziheng Ding, Qi Jing, Yuejie Zhang, Wenchao Ding, Rui Feng
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate our proposed Deep Point Map and current state-of-the-art algorithms on benchmark datasets. |
| Researcher Affiliation | Academia | 1School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University 2Academy for Engineering and Technology, Fudan University 3Shanghai Collaborative Innovation Center of Intelligent Visual Computing |
| Pseudocode | No | The paper describes procedures but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | 1Source code: https://github.com/Zhang Xiaze/Deep Point Map |
| Open Datasets | Yes | Semantic KITTI (Behley et al. 2019, 2021) is a widely used benchmark dataset... KITTI-360 (Liao, Xie, and Geiger 2022) includes... Mul Ran (Kim et al. 2020) is a range dataset... KITTI-Carla (Deschaud 2021) is a synthetic dataset... |
| Dataset Splits | Yes | We use the first 6 sequences as training-set. ... The split of training and testing set is based on the frame amount of approx. 6:4, without any manual picking. |
| Hardware Specification | Yes | on 6 RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions Open3D library and Adam W optimizer, but specific version numbers for these software dependencies are not provided. |
| Experiment Setup | Yes | with Adam W optimizer (Reddi, Kale, and Kumar 2019), initial learning rate lr = 10 3, weight decay wd = 10 4, cosine lr scheduler, on 6 RTX 3090 GPU. For all tasks, the network is trained for 12 epochs. ... We use the average of loss in both directions, with the hyper-parameters of εpositive = 1m, εoffset = 2m, τ = 0.1, λ1, λ2, λ3 = (1.0, 0.1, 1.0). |