cpp/README.md

RCLane native C++ runtime

This directory is the sequential deployment runtime. One frame completes preprocessing, TensorRT inference, 1024-seed decode and raw-model BEV export before the next frame begins. There is no inter-frame overlap. Rendering, source-video decoding and video writing are outside the measured core latency.

Raw BEV remains the default and is a one-to-one projection of decoded model lanes. Optional BEV-only topology modes can detect collapsed/crossing curves, force detected curves to be parallel, or synthesize a complete P0-P3 set. The decoded camera-space lanes are never modified or back-projected.

Select a mode with --bev-mode:

• raw: measured cubics only; funnel clipping may shorten their X domain.
• trigger: repair only when an adjacent gap collapses or crosses.
• always-parallel: force all valid detected cubics to share one parallel reference geometry; missing lanes remain missing.
• complete-four: always export parallel P0-P3, synthesizing missing markings; this display/output prior intentionally bypasses camera-funnel clipping.

Python-compatible aliases are also accepted: --disable-parallel-repair, --parallel-repair, --always-parallel-repair, and --complete-four-parallel-lanes.

The build intentionally consumes TensorRT/CUDA SDK headers and shared libraries from the target machine; serialized TensorRT engines must be rebuilt per GPU.

cmake -S cpp -B cpp/build -G "Unix Makefiles" \
  -DTENSORRT_INCLUDE_DIR=/path/to/TensorRT/include \
  -DTENSORRT_LIBRARY=/path/to/libnvinfer.so \
  -DCUDA_INCLUDE_DIR=/path/to/cuda/include \
  -DCUDART_LIBRARY=/path/to/libcudart.so
cmake --build cpp/build -j8

TensorRT and CUDA are auto-discovered in standard x86/Jetson locations. The explicit -D paths above are useful for Python-wheel installations.

One-frame parity harness

The runtime accepts either a preprocessed float32 NCHW tensor or a raw 1920x1080 BGR frame. It can dump maps, decoded image lanes and metric BEV cubics for comparison with Python:

cpp/build/rclane_runtime \
  --engine exports/trt_cache/model.engine \
  --input-bgr /tmp/frame.bgr \
  --dump-prefix /tmp/cpp \
  --lanes-json /tmp/cpp_lanes.json \
  --bev-json /tmp/cpp_bev.json \
  --threads 8

For example, enable trigger-only repair with --bev-mode trigger, or always complete four parallel BEV markings with --bev-mode complete-four.

Sequential full-video benchmark

Pipe decoded BGR frames from FFmpeg. The reported core_pipeline contains only preprocess + TensorRT (including transfers) + decode + BEV/cubic/funnel.

ffmpeg -loglevel error -i raw_Town04_Opt_20260714_093110.mp4 \
  -f rawvideo -pix_fmt bgr24 - | \
cpp/build/rclane_runtime \
  --engine exports/trt_cache/model.engine \
  --raw-bgr-stdin --source-width 1920 --source-height 1080 \
  --threads 8 --warmup 10 --timing-warmup 5 \
  --frames-jsonl runs/cpp_final_lanes.jsonl \
  --report runs/cpp_benchmark_final_full.json

Render those saved C++ results later without rerunning inference or affecting the measured pipeline latency:

python cpp/render_cpp_results.py \
  --video raw_Town04_Opt_20260714_093110.mp4 \
  --results runs/cpp_final_lanes.jsonl \
  --benchmark-report runs/cpp_benchmark_final_full.json \
  --output runs/cpp_final_render_h264.mp4

On the development RTX 3050/i5-13420H machine, the complete 1768-frame final video measured 13.39 ms median and 14.15 ms p95 core latency (74.67 FPS median) with 1024 seeds and 8 CPU threads. TensorRT engines are GPU-architecture specific and must be rebuilt on the deployment target.