EDUCATION & TRAINING
Our LiDAR detector spent 40% of its time in voxelization, not convs
Dev.to Machine Learning
About This Tutorial
TL;DR: We profiled a LiDAR object detector expecting the 3D backbone to dominate. It didn't. Voxelization plus the scatter-to-pillars step ate roughly 40% of per-frame latency on an A100, and pulling them out of the Python hot path took our p50 from 31ms down to 19ms. The assumption that cost us two weeks When I was at Valeo.ai we ran a PointPillars-style detector on nuScenes-scale point clouds, around 250k points per sweep. The mental model everyone carried was simple. The sparse con backbone is heavy, so the backbone is where the milliseconds go.