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..
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Authors: Yurong You, Cheng Perng Phoo, Katie Luo, Travis Zhang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with our approach on two large-scale driving datasets and show remarkable improvement in 3D object detection of cars, pedestrians, and cyclists, bringing us a step closer to generalizable autonomous driving. |
| Researcher Affiliation | Academia | 1Cornell University, Ithaca NY 2The Ohio State University, Columbus, OH |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Yurong You/ Rote-DA. |
| Open Datasets | Yes | We validate our approach on a single source dataset, the KITTI dataset [8] and two target datasets: the Lyft Level 5 Perception dataset [11] and the Ithaca-365 dataset [5]. |
| Dataset Splits | No | The paper specifies train/test splits (e.g., "This results a train /test split of 11,873/4,901 point clouds for the Lyft dataset.") but does not explicitly mention a validation dataset split. |
| Hardware Specification | Yes | All models are trained/fine-tuned with 4 GPUs (NVIDIA 2080Ti/3090/A6000). |
| Software Dependencies | No | The paper mentions "We use the default implementation/configuration of Point RCNN [26] from Open PCDet [19]" but does not specify version numbers for Open PCDet or any other software dependencies. |
| Experiment Setup | Yes | For fine-tuning, we fine-tune the model for 10 epochs with learning rate 1.5 10 3 (pseudo-labels are regenerated and refined after each epoch). In our experiments, we set αFB-F = 20 and γFB-F = 0.5 (We find these values are not sensitive). We use 5 traversals to compute PP-score for each scene. |