RETR: Multi-View Radar Detection Transformer for Indoor Perception
Authors: Ryoma Yataka, Adriano Cardace, Perry Wang, Petros Boufounos, Ryuhei Takahashi
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluated on two indoor radar perception datasets, our approach outperforms existing stateof-the-art methods by a margin of 15.38+ AP for object detection and 11.91+ Io U for instance segmentation, respectively. |
| Researcher Affiliation | Collaboration | 1Mitsubishi Electric Research Laboratories (MERL), USA 2Department of Computer Science and Engineering, University of Bologna, Italy 3Information Technology R&D Center (ITC), Mitsubishi Electric Corporation, Japan |
| Pseudocode | No | The paper describes the architecture and processes in text and diagrams (Figure 3, 7, 8) but does not present them in formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/merlresearch/radar-detection-transformer. |
| Open Datasets | Yes | We evaluate performance over two open indoor radar perception datasets: MMVR4 [26] and HIBER5 [38]. https://zenodo.org/records/12611978 https://github.com/Intelligent-Perception-Lab/HIBER |
| Dataset Splits | Yes | For the training-validation-test split, we follow the data split S1 as defined in MMVR. Table 5: Details of hyper-parameters. ... # of training 190441 / 118280 # of validation 23899 / 33841 # of test 23458 / 85677 |
| Hardware Specification | Yes | Table 5: Details of hyper-parameters. ... GPU (NVIDIA) A40 |
| Software Dependencies | No | The paper mentions using 'Res Net' as a backbone, but does not specify software dependencies like programming language versions (e.g., Python 3.x) or library versions (e.g., PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | Table 5: Details of hyper-parameters. The table lists specific values for parameters such as 'Total dimension of positional embedding', 'Ratio of depth dimension for TPE', '# of input frames', 'Top-K selection magnitude', '# of encoder blocks', '# of decoder blocks', '# of head of multi-head attention', '# of queries', 'Threshold for detection and segmentation', 'Loss weight for GIo U on horizontal plane', 'Loss weight for L1 on horizontal plane', 'Batch size', 'Epoch for detection', 'Epoch for segmentation', 'Patience for early stopping', 'Learning rate', 'Sheduler', and 'Weight decay'. |