Enhancing Generalizability via Utilization of Unlabeled Data for Occupancy Perception
Authors: Ruihang Li, Tao Li, Shanding Ye, Kaikai Xiao, Zheng Huangnan, Zhe Yin, Zhijie Pan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiment results on two challenging datasets nu Scenes and Waymo, demonstrate that our method not only achieves state-of-the-art generalizability but also enhances the model s perceptual capabilities within the source domain by utilizing unlabeled data. Quantitative Experiments and Ablation Studies |
| Researcher Affiliation | Academia | Ruihang Li, Tao Li, Shanding Ye, Kaikai Xiao, Zheng Huangnan, Zhe Yin, Zhijie Pan College of Computer Science and Technology, Zhejiang University Hangzhou, 310027 China EMAIL |
| Pseudocode | No | The paper describes methods and provides equations, but it does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Additionally, we have open-sourced our code, contributing to the community. |
| Open Datasets | Yes | Our experiments use the nu Scenes and Waymo datasets (Caesar et al. 2020; Sun et al. 2020) with OCC labels from OCC-nu Scenes and OCC-Waymo. |
| Dataset Splits | No | The paper defines 'nu Scenes as the source domain and Waymo as the target domain' for benchmarks, and vice-versa, but does not provide specific training/validation/test splits (e.g., percentages or sample counts) for these datasets. |
| Hardware Specification | Yes | Experiments run on six 4090 devices |
| Software Dependencies | No | The paper mentions using Res Net and Adam W optimizer, but does not provide specific version numbers for any software libraries or dependencies (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Methods are trained for 24 epochs in the source domain using the Adam W optimizer with a learning rate of 7 x 10^-5. For cross-domain learning, we pretrain self-supervised depth estimation and Semantic Query Adversarial Fusion, setting their backpropagation ratios to 0.1. Confidence and smoothness thresholds are 0.85 and 0.8, respectively. Experiments run on six 4090 devices, with a learning rate of 3.5 x 10^-5, continuing until the pseudo-label selection ratio exceeds 35%. |