LaserPerception v0.1.0 delivers 136x speedup for LiDAR AI
Open-source project hits 10Hz sustained LiDAR inference with exact determinism
Mahad Azher has released **LaserPerception v0.1.0**, an open-source LiDAR perception deployment project designed to bridge the gap between academic models and real-world autonomous systems. The framework combines PointPillars architecture, TensorRT FP16 optimization, and ROS 2 for reproducible 3D LiDAR detection. Unlike typical releases that focus on training scripts, this project prioritizes production-ready inference pipelines with exact determinism.
The release delivers impressive performance gains through meticulous optimization. The hard-voxel layer alone saw a 136x speedup (from 238.9ms to 1.76ms), while sustaining 10Hz inference on an RTX 4060 Laptop GPU under WSL2. The project maintains bit-exact results across 81 nuScenes mini_val samples and 20 frozen detector samples, rejecting non-deterministic optimizations that could alter detection outputs. The repo also documents failed approaches, including rejected execution-boundary comparisons and the abandoned 20Hz target, providing a transparent view of the development process.
- LaserPerception v0.1.0 combines PointPillars + TensorRT FP16 + ROS 2 for production LiDAR inference
- Achieves 10Hz sustained performance on RTX 4060 GPU with exact deterministic voxelization (81/81 nuScenes samples match official implementation)
- Hard-voxel layer optimization delivers 136x speedup (238.9ms → 1.76ms) while maintaining bit-exact results
Why It Matters
Enables real-time LiDAR perception for autonomous systems with verifiable performance and reproducible results