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Blurry – Host, share, and embed Gaussian Splatting models

Hacker News

Blurry – Host, share, and embed Gaussian Splatting models

Hey HN, I noticed that there is a few tools to create and edit 3D Gaussian Splatting[1] models, but not so many tools for robust hosting, sharing, and presentation of these models, so I built Blurry! Unlike other platforms, I’m specifically focusing on the best 3DGS in-browser viewer experience you can seamlessly embed on websites or Notion docs, with more places coming soon! Some of the potential use cases are professional training, product and rental space marketing, and construction business. But at this stage with Blurry, I’m specifically targeting people/businesses that already use 3D scanning to a certain degree but are lacking an easy-to-use and performant hosting platform for their 3DGS models. I’m shipping new features and improvements very quickly. Two big things on the current roadmap are first-person camera controls (especially for indoor splats), and a support for really large models (possibly done by streaming of the model as you move around). Would love your thoughts and feedback pls! [1] For those who don’t know, 3D Gaussian Splatting is a fairly new method to reconstruct a 3D model from real pictures and videos ( https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/ ).

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, host, soon · Missing: plus, intuitive, reviews
73%73% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
15%15% 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.

Incorrect prediction on native model

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