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..
Compact HD Map Construction via Douglas-Peucker Point Transformer
Authors: Ruixin Liu, Zejian Yuan
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the public nu Scenes dataset demonstrate that our method overwhelms current SOTAs. Extensive ablation studies validate each component of our methods. |
| Researcher Affiliation | Academia | Ruixin Liu, Zejian Yuan* Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: DP point generation. |
| Open Source Code | Yes | Codes will be released at https://github.com/sweety121/DPFormer. |
| Open Datasets | Yes | Experiments are conducted on the large-scale nu Scenes (Caesar et al. 2020) dataset |
| Dataset Splits | Yes | we follow (Li et al. 2022a; Liu et al. 2023b; Liao et al. 2023) to train on the 700 scenes (28130 samples) from the training set and test on the 150 scenes (6019 samples) from the validation set. |
| Hardware Specification | Yes | All experiments are conducted on a single Nvidia RTX 3090. |
| Software Dependencies | No | The paper mentions software components like ResNet50 and AdamW optimizer, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We train DPFormer with a batch size of 4 (each containing K = 6 images). The Adam W (Loshchilov and Hutter 2019) optimizer is adopted with a learning rate of 1.25e 4 for single-card training. According to data statistics shown in Figure 2, we set the maximum number of DP points as N = 8, the maximum number of all points as M = 20, and the maximum number of map elements as J = 50. Besides, the loss coefficients αc1, αc2, αp and αs are set to 2, 0.5, 5, and 0.5, respectively. Ablation studies are trained with 50 epochs. |