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Seed3D – Generate Physics-Ready 3D Models with Millimeter Precision

Hacker News

Seed3D – Generate Physics-Ready 3D Models with Millimeter Precision

I built Seed3D, a next-generation 3D reconstruction and modeling tool that turns 2D inputs into solid, physics-ready 3D models — with millimeter precision, physically accurate PBR materials, and export formats ready for any major engine. Unlike traditional photogrammetry or AI mesh tools, Seed3D doesn’t fake geometry with normal maps — it reconstructs real surfaces, edges, and micro-details for simulation-grade accuracy and realism. What it does • Generates watertight polygonal meshes with crisp, true geometry • Outputs full PBR texture sets (albedo, roughness, metalness, normal, AO) • Delivers 6K textures that hold up in extreme close-ups • Ensures physics-stable topology for simulations and collisions • Exports USD/USDZ, FBX, and GLTF — compatible with Omniverse, Unity, and Unreal Engine Who it’s for • Embodied AI and robotics researchers • Game, XR, and VFX developers • Product visualization and digital twin creators • Simulation and graphics educators Try the Seed3D 3D model generator here: https://www.seed3d.xyz/ Would love feedback from the HN community — especially on fidelity, physics performance, and engine integration.

Visit product View on Hacker NewsOct 24, 2025

Actual performance

5points

2comments

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

89%89% 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, models, visual · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, 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

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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