Compact HD Map Construction via Douglas-Peucker Point Transformer
Authors: Ruixin Liu, Zejian Yuan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 sweetylrx@stu.xjtu.edu.cn, yuan.ze.jian@xjtu.edu.cn |
| 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. |