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 | Conference PDF | Archive PDF | Plain Text | 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. |