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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |