Papers › Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Mu Chen, Zhedong Zheng, Yi Yang
Scene segmentation via unsupervised domain adaptation (UDA) enables the transfer of knowledge acquired from source synthetic data to real-world target data, which largely reduces the need for manual pixel-level annotations in the target domain. To facilitate domain-invariant feature learning, existing methods typically mix data from both the source domain and target domain by simply copying and pasting the pixels. Such vanilla methods are usually sub-optimal since they do not take into account how well the mixed layouts correspond to real-world scenarios. Real-world scenarios are with an inherent layout. We observe that semantic categories, such as sidewalks, buildings, and sky, display relatively consistent depth distributions, and could be clearly distinguished in a depth map. Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i.e., segmentation and depth learning in an end-to-end manner. In particular, the framework contains a Depth-guided Contextual Filter (DCF) forndata augmentation and a cross-task encoder for contextual learning. DCF simulates the real-world layouts, while the cross-task encoder further adaptively fuses the complementing features between two tasks. Besides, it is worth noting that several public datasets do not provide depth annotation. Therefore, we leverage the off-the-shelf depth estimation network to generate the pseudo depth. Extensive experiments show that our proposed methods, even with pseudo depth, achieve competitive performance on two widely-used bench-marks, i.e. 77.7 mIoU on GTA to Cityscapes and 69.3 mIoU on Synthia to Cityscapes.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | GTA5 to Cityscapes | DCF | mIoU | 77.7 | #2 of 28 | Archive leaderboard | report |
| Domain Adaptation | SYNTHIA-to-Cityscapes | DCF | mIoU | 69.3 | #3 of 33 | Archive leaderboard | report |
| Synthetic-to-Real Translation | GTAV-to-Cityscapes Labels | DCF | mIoU | 77.7 | #1 of 73 | Archive leaderboard | report |
| Synthetic-to-Real Translation | SYNTHIA-to-Cityscapes | DCF | MIoU (13 classes) | 75.9 | #1 of 38 | Archive leaderboard | report |
| Synthetic-to-Real Translation | SYNTHIA-to-Cityscapes | DCF | MIoU (16 classes) | 69.3 | #1 of 38 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | SYNTHIA-to-Cityscapes | DCF | MIoU (16 classes) | 69.3 | #1 of 23 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | SYNTHIA-to-Cityscapes | DCF | mIoU | 69.3 | #1 of 23 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | SYNTHIA-to-Cityscapes | DCF | mIoU (13 classes) | 75.9 | #1 of 23 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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