{"id": "1t4hlq0", "title": "Built a classical perception pipeline (no deep learning for detection) on infrastructure LiDAR - here's what actually broke", "subreddit": "LiDAR", "url": "https://www.reddit.com/r/LiDAR/comments/1t4hlq0/built_a_classical_perception_pipeline_no_deep/", "score": 63, "comments": 2, "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": 1777992394.0}