I’ve released LaserPerception v0.1.0, an open-source LiDAR deployment project focused on reproducible PointPillars/TensorRT/ROS 2 inference rather than model training.
Representative W1 workload: current keyframe + 10 historical sweeps, 354,182 points, RTX 4060 Laptop GPU under WSL2.
10 Hz: sustained cleanly, ~9.95 Hz output, zero measured drops
15 Hz: not sustained, ~13.34 Hz useful output
20 Hz: not sustained, ~10.83 Hz useful output
So the release deliberately does not claim a 20 Hz ROS pipeline.
A second result came from deterministic hard voxelization. I first tried the faster deterministic=False path, but it changed retained point subsets and produced observable detector differences, so I rejected it.
The replacement exact_fast path matched the official deterministic implementation:
81/81 nuScenes mini_val samples bit-exact
20/20 frozen detector samples with exact raw TensorRT outputs
no custom CUDA/C++
no TensorRT plugin
Representative hard-voxel layer measurements:
238.9 ms → 1.76 ms
261.9 ms → 1.92 ms
That’s roughly 136× at the hard-voxel layer only, not end-to-end.
I also posted the result to the original MMDetection3D voxelization discussion:
One thing I deliberately kept in the repo is the failed/corrected benchmark history: lighter scene-start workloads, rejected execution-boundary comparisons, the nondeterministic shortcut, and the failed 20 Hz target.
I’d be interested in how other ROS 2 perception teams report performance:
model runtime, callback latency, sensor-to-output loopback, or all three?
