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 [1].
R4D: Utilizing Reference Objects for Long-Range Distance Estimation
Authors: Yingwei Li, Tiffany Chen, Maya Kabkab, Ruichi Yu, Longlong Jing, Yurong You, Hang Zhao
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the two proposed datasets demonstrate the effectiveness and robustness of R4D by showing significant improvements compared to existing baselines. |
| Researcher Affiliation | Industry | Waymo LLC EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states that the Waymo Open Dataset Long-Range Labels will be made available publicly, but does not provide a concrete access statement or link for the source code of the R4D methodology itself. |
| Open Datasets | Yes | We re looking to make this dataset available publicly at waymo.com/open/download. |
| Dataset Splits | Yes | the Pseudo Long-Range KITTI Dataset contains 2,181 images with 4,233 vehicles, and 2,340 images with 4,033 vehicles in the training and validation sets, respectively. |
| Hardware Specification | Yes | We train R4D on an 8-core TPU with a total batch size of 32 images for 24 epochs. |
| Software Dependencies | No | The paper mentions software components like ResNet-50, ROIAlign, smooth L1, and Layer Normalization, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We train R4D on an 8-core TPU with a total batch size of 32 images for 24 epochs. The learning rate is initially set to 0.0005 and then decays by a factor of 10 at the 16th and 22nd epochs. We warm up (Goyal et al., 2017) the learning rate for 1,800 iterations. |