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ARXIV:2604.04402 · VIDEO DERAINING · SUBMITTED 07 APR · 20:12 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04402VIDEO DERAININGSUBMITTED 07 APR · 20:12 UTCFRESHNESS UNKNOWNPei Yang · Hai Ci · Beibei Lin · Yiren Song · Mike Zheng Shou · arXiv
A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios.
Opportunity summary
Pain A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios. Unlike daytime white rain, nighttime rain takes on various colors and appears locally illuminated.
Nighttime video deraining is uniquely challenging because raindrops interact with artificial lighting. Unlike daytime white rain, nighttime rain takes on various colors and appears locally illuminated.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive benchmarking demonstrates that models trained on our dataset generalize significantly better to real-world videos. Code availability is flagged in the production record; the…
Video Deraining moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios.
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Paper Pack
10.48550/arXiv.2604.04402A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios.
Abstract
Nighttime video deraining is uniquely challenging because raindrops interact with artificial lighting. Unlike daytime white rain, nighttime rain takes on various colors and appears locally illuminated. Existing small-scale synthetic datasets rely on 2D rain overlays and fail to capture these physical properties, causing models to generalize poorly to real-world night rain. Meanwhile, capturing real paired nighttime videos remains impractical because rain effects cannot be isolated from other degradations like sensor noise. To bridge this gap, we introduce UENR-600K, a large-scale, physically grounded dataset containing 600,000 1080p frame pairs. We utilize Unreal Engine to simulate rain as 3D particles within virtual environments. This approach guarantees photorealism and physically real raindrops, capturing correct details like color refractions, scene occlusions, rain curtains. Leveraging this high-quality data, we establish a new state-of-the-art baseline by adapting the Wan 2.2 video generation model. Our baseline treat deraining as a video-to-video generation task, exploiting strong generative priors to almost entirely bridge the sim-to-real gap. Extensive benchmarking demonstrates that models trained on our dataset generalize significantly better to real-world videos. Project page: https://showlab.github.io/UENR-600K/.
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Dimensions overall score 7.0
PROBLEM
A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios. Unlike daytime white rain, nighttime rain takes on various colors and appears locally illuminated.
METHOD
Nighttime video deraining is uniquely challenging because raindrops interact with artificial lighting. Unlike daytime white rain, nighttime rain takes on various colors and appears locally illuminated.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive benchmarking demonstrates that models trained on our dataset generalize significantly better to real-world videos. Code availability is flagged in the production record; the public repository li...
WHY NOW
Video Deraining moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios. Unlike daytime white rain, nighttime rain takes on various colors and appears locally illuminated.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Nighttime video deraining is uniquely challenging because raindrops interact with artificial lighting. Unlike daytime white rain, nighttime rain takes on various colors and appears locally illuminated.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive benchmarking demonstrates that models trained on our dataset generalize significantly better to real-world videos. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video Deraining moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A large-scale, physically grounded dataset for nighttime video deraining, enabling significantly better generalization to real-world scenarios.
Segment
Video Deraining
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Commercial read
7.0/10 public viability
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Technical feasibility
partial
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