Achieving a 155× Speedup in a LiDAR Data Processing Pipeline

We present a case study of systematically optimizing a real-world, multi-stage data processing pipeline for high-resolution LiDAR road condition assessment. The original system, grown over more than a decade, required weeks of computation for a typical measurement campaign covering a 10 km road network, processing approximately 200 GB of raw data. Our contribution is the end-to-end optimization of a heterogeneous, production pipeline through a combination of hardware consolidation (from a distributed eight-server architecture to a single high-performance node), elimination of intermediate file I/O through stream processing, migration from heterogeneous runtimes (MATLAB, Python, C++) to a single codebase in managed C# code, and pervasive SIMD vectorization and parallelization. The key enabler was a continuous, measurement-driven methodology: automated benchmarks tracked per-commit performance across reference datasets, allowing us to systematically identify and eliminate the dominant bottleneck at each stage of the project. We achieved a 155x end-to-end speedup, enabling the pipeline to process data faster than it is recorded. We do not claim novel algorithms; rather, we demonstrate that disciplined engineering, including profiling, architectural simplification, and exploitation of modern hardware capabilities, can yield order-of-magnitude improvements in real-world, legacy data processing systems.