{"id": "1t4hjy5", "title": "Lessons from building a real-world LiDAR perception pipeline (failures, tradeoffs, what broke)", "subreddit": "computervision", "url": "https://www.reddit.com/r/computervision/comments/1t4hjy5/lessons_from_building_a_realworld_lidar/", "score": 272, "comments": 15, "selftext": "I recently built an end-to-end perception pipeline on 128-beam infrastructure-mounted LiDAR — the kind you'd see on a pole at an intersection, not on a vehicle. 184k points per frame, 10 sequential frames, busy urban scene. Ground removal → clustering → classification → tracking. All classical methods, no neural nets for detection.\n \nI want to share the parts that surprised me most, because they're not the parts you'd expect.\n \n---\n \n**Ground removal was harder than classification.**\n \nI went th", "created_utc": 1777992285.0}