SDDiff: Boosting Radar Perception via Spatial-Doppler Diffusion
Authors: Shengpeng Wang, Xin Luo, Yulong Xie, Wei Wang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations show that SDDiff significantly outperforms stateof-the-art baselines by achieving 59% higher in EVE accuracy, 4 greater in valid generation density while boosting PCE effectiveness and reliability. |
| Researcher Affiliation | Academia | 1Huazhong University of Science and Technology 2Wuhan University EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods in prose and equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and dataset will be available on https://github.com/Stellar Esti/SDDiff. |
| Open Datasets | Yes | Additionally, we will make our self-collected dataset publicly available to the research community. We evaluate the proposed method using both the publicly available Coloradar dataset and a self-collected dataset across different indoor and outdoor scenarios. Colo Radar Dataset. We conduct our method on Colo Radar Dataset [Kramer et al., 2022] |
| Dataset Splits | Yes | For a fair comparison with other learning-based baselines, we select the same 36 sequences as the training set and others for testing. The dataset comprises 10,371 frames, with 10% used for fine-tuning and 90% for testing. |
| Hardware Specification | Yes | It takes about 5 days to train our model with a machine using three NVIDIA GeForce RTX 4090 GPUs and Intel Xeon Gold 6226R CPU. |
| Software Dependencies | Yes | We implement our SDDiff using Pytorch 1.11.0 with CUDA 12.4. |
| Experiment Setup | Yes | The parameters ω of the weighted spatial and Doppler loss are set to 0.01. We train SDDNet for 100 epochs on Colo Radar Dataset with Adam W optimizer and a learning rate 10 4. |