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 autonomous driving systems. Most of the works in semantic segmentation pertains to day-time scenes with favourable weather and illumination conditions. In this paper, we propose a novel deep architecture, NiSeNet, that performs semantic segmentation of night scenes using a domain mapping approach of synthetic to real data. It is a dual-channel network, where we designed a Real channel using DeepLabV3+ coupled with an MSE loss to preserve the spatial information. In addition, we used an Adaptive channel reducing the domain gap between synthetic and real night images, which also complements the failures of Real channel output. Apart from the dual channel, we introduced a novel fusion scheme to fuse the outputs of two channels. In addition to that, we compiled a new dataset Urban Night Driving Dataset (UNDD); it consists of 7125 unlabelled day and night images; additionally, it has 75 night images with pixel-level annotations having classes equivalent to Cityscapes dataset. We evaluated our approach on the Berkley Deep Drive dataset, the challenging Mapillary dataset and UNDD dataset to exhibit that the proposed method outperforms the state-of-the-art techniques in terms of accuracy and visual quality.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections