Papers › What's There in the Dark
What's There in the Dark
Sauradip Nag, Saptakatha Adak, Sukhendu Das
Scene Parsing is an important cog for modern autonomousdriving systems. Most of the works in semantic segmenta-tion pertains to day-time scenes with favourable weather andillumination conditions. In this paper, we propose a noveldeep architecture, NiSeNet, that performs semantic segmen-tation of night scenes using a domain mapping approach ofsynthetic to real data. It is a dual-channel network, wherewe designed a Real channel using DeepLabV3+ coupled withan MSE loss to preserve the spatial information. In addition,we used an Adaptive channel reducing the domain gap be-tween synthetic and real night images, which also comple-ments the failures of Real channel output. Apart from thedual channel, we introduced a novel fusion scheme to fuse theoutputs of two channels. In addition to that, we compiled anew dataset Urban Night Driving Dataset (UNDD); it consistsof7125unlabelled day and night images; additionally, it has75night images with pixel-level annotations having classesequivalent to Cityscapes dataset. We evaluated our approachon the Berkley Deep Drive dataset, the challenging Mapil-lary dataset and UNDD dataset to exhibit that the proposedmethod outperforms the state-of-the-art techniques in termsof accuracy and visual quality
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | BDD100K val | NiseNet | mIoU | 53.52 | #9 of 24 | Archive leaderboard | report |
| Semantic Segmentation | Mapillary val | NiseNet | mIoU | 48.32 | #5 of 8 | 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.
Methods
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