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AI-Powered Lidar Point Cloud Classification

Hacker News

AI-Powered Lidar Point Cloud Classification

I spent years manually editing large LiDAR point clouds—and I hated every moment of doing this. To make things easier, my team and I did extensive research and development on the latest state-of-the-art techniques for point cloud processing. We built a massive training dataset and trained semantic segmentation networks, all packaged into an AI-powered platform called Flai. With Flai, you can upload, view, and classify your point clouds into over 30 categories, including buildings, power lines, vehicles and many more. It’s free to try ( https://app.flai.ai/register ), and I’d love to hear your feedback!

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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