RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar
Authors: Fangqiang Ding, Xiangyu Wen, Yunzhou Zhu, Yiming Li, Chris Xiaoxuan Lu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the K-Radar dataset show Radar Occ s state-of-the-art performance in radar-based 3D occupancy prediction and comparable results to other modalities in normal weathers. |
| Researcher Affiliation | Academia | 1University of Edinburgh 2Georgia Institute of Technology 3New York University 4AI Centre, Department of Computer Science, UCL |
| Pseudocode | No | The paper describes its method through text and a pipeline diagram (Fig. 1) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code and model at https: //github.com/Toytiny/Radar Occ. |
| Open Datasets | Yes | Our experiments are conducted on the K-Radar dataset [42], which is, to the best of our knowledge, the only autonomous driving dataset providing available 4DRT data. |
| Dataset Splits | Yes | We split the annotated 24 sequences into the training, validation and test sets with a ratio of 17:2:5, resulting in 11,333, 1,059 and 2,878 frames, respective. |
| Hardware Specification | Yes | All of our experiments are conducted on a Ubuntu server equipped with 2 Nvidia RTX 3090 24GB GPUs, an Intel i9-10980XE CPU @ 3.00GHz and a 64GB RAM. |
| Software Dependencies | No | The paper mentions software like the 'spconv library' and 'PyTorch' but does not specify their version numbers, which is necessary for reproducible ancillary software details. |
| Experiment Setup | Yes | We train Radar Occ with 10 epochs using Adam optimizer with a learning rate of 3e-4. The batch size is 1 for each GPU. We follow [3] to use loss normalization to balance the weight of the 4 different losses, and cosine annealing [107] with 1/3 warm-up ratio is used at the start of the training. |