A point cloud viewer that renders 500M points at 60fps

Hacker News

Problem: Large-scale 3D point cloud datasets, with hundreds of millions of points, are often very slow or fail to open in existing desktop viewers due to memory limitations. Solution: * I designed a hybrid architecture: a Rust native module handles the heavy data processing, while WebGPU manages the rendering. * Instead of loading the entire dataset into memory, I implemented a custom Level of Detail (LOD) system that dynamically loads only the necessary tiles. * Thanks to this architecture, it's theoretically possible to visualize a limitless number of points, provided you have the storage space. Result: The result is 'PointPeek', a desktop app that smoothly explores a 500M point (10GB) dataset at 60fps. Check it out in the demo video below: * Demo Video: https://x.com/leesyub/status/1963623557117731144 Current Challenge: I'm currently tackling the challenge of rendering the entire scanned point dataset of the city of Vancouver, Canada. (Though, just downloading the data seems like it will take a few days.) Ultimate Goal: After that, the ultimate goal is to integrate a local AI model via Ollama to implement features for querying and analyzing the data using natural language. Technical questions or feedback are welcome.

Visit product View on Hacker NewsSep 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, visual, using · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, llama · Missing: https docs, excited, just released

50%50% 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 · Strong signals: video, visualize · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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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