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Interp-n

Turning 30-min satellite snaps into smooth storm movies — physics-aware frame interpolation at 24ms/patch with zero seams 4D RAFT flow + edge-preserving refinement

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This is 1 of 147 launches in video generation and vision infrastructure — see how it stacks up on momentum and crowding →

957 other launches read as similar to this one →

Details

External ID
1412583094
Source
GITHUB
Company
—
Product
Interp-n
Website domain
github.com
Launched
Oct. 10, 2026
Cohort
—
Upvotes
11
Upvotes percentile
0.14
Tags
—

Enrichment

Niche
video generation and vision infrastructure
Vertical
Energy & climate
Function
Data infrastructure
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
frame interpolation model for satellite storm imagery
Manually corrected
False

Could you build this?

No Physics-aware 4D optical flow estimation (RAFT) and low-latency satellite frame interpolation require specialized computer vision research, custom GPU tensor kernels, and atmospheric physics domain modeling.

What it would actually take: Requires training and optimizing a custom 4D RAFT (Recurrent All-Pairs Field Transforms) model on sparse satellite imagery (e.g., GOES/Himawari datasets), implementing edge-preserving spatial-temporal loss functions, and optimizing inference with TensorRT/CUDA to meet the 24ms per patch constraint. The core challenge is handling non-rigid cloud deformations and advective atmospheric physics where standard video interpolation models create unnatural warping artifacts. Requires advanced expertise in deep learning, computer vision, and GPU performance profiling.

Competitors

Other products that read as similar to this one — 957 launches clear the similarity bar, closest 8 shown.

Attention rank: #797 of 958 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 343 days after the earliest competitor.

Other launches for this product