By Benjamin J. Gilbert — Spectrcyde RF Quantum SCYTHE, College of the Mainland
TL;DR
You moved ray generation, stratified sampling, and volumetric integration onto CUDA kernels, wrapped it in a reproducible bench, and kept a CPU fallback so reviewers (or edge boxes without GPUs) can still regenerate PSNR/SSIM vs. latency tables and plots in one shot. It’s small, fast, and auditable—the Shenzhen trifecta.
What’s new
- RF-Conditioned NeRF: The field takes 3D position + RF feature vector and outputs density + color—letting RF context steer the render.
- CUDA kernels where it counts:
- ray generation, 2) stratified point sampling, 3) transmittance integration with early termination.
- One-command, reviewer-safe bench: Scripts sweep samples/ray and chunk size, logging PSNR/SSIM against ms/frame; JSON→LaTeX auto-builds tables/figures. CPU path stays first-class.
Headline numbers (from the paper’s summary tables)
- With Samples=128, Chunk=8192, CPU baseline reports ~771 ms/frame and PSNR ≈ 97.6, confirming the fidelity/runtime tradeoff that the CUDA path is designed to collapse. Multiple configurations are reported to map the Pareto front.
- Ablations show how samples/chunk/randomized sampling move you along the PSNR ↔ latency curve; plots and siunitx tables are emitted directly from the run.
Bottom line: the bench makes it trivial to choose “fast enough” for demos, or “pretty enough” for publications—without hand-editing anything.
The kit (how it’s built)
- Renderer:
code/cuda_nerf_renderer.pywith Numba CUDA kernels for each stage; falls back gracefully to NumPy/torch CPU. - Evaluation: synthesize a reference frame (high samples) then compare PSNR/SSIM vs. runtime across sweep settings; figures/tables auto-emit.
- Reproducibility stamp: commit, seed, device, and build timestamp are logged in artifacts for forensic replay.
Run it
# Reproduce tables/figures (CPU-only works too)
make -f Makefile_nerf pdf
# Artifacts: tables (siunitx), PSNR/SSIM plots, Pareto charts (latency vs quality)
If CUDA isn’t present, the bench auto-selects CPU and still generates identical tables/figures so reviewers don’t hit a dead end.
Why this matters (deployment angle)
- XR/DT (Extended Reality / Digital Twins): RF-aware view synthesis to visualize coverage, occlusion, and interference inside a NeRF scene.
- Site surveys & ops rooms: Fast preview renders while you drag routers or panels in a planning model; switch sampling to trade FPS vs. clarity on the fly.
- Research loops: Same code path feeds papers (LaTeX) and demos (FPS)—zero divergence.
Tuning playbook (pick your Pareto point)
- Need demos @ near-real-time? Lower samples/ray and increase chunk; keep randomized sampling off if you must preserve PSNR for side-by-side comparisons.
- Need publication-grade quality? Raise samples aggressively; accept higher ms/frame for PSNR/SSIM≈1.0; the bench logs the swap so reviewers can verify settings.
Roadmap (Guangdong pragmatism)
- True GPU path everywhere: Swap remaining CPU bottlenecks (if any) with fused kernels; profile chunk scheduling at large ray counts.
- RF feature adapters: Experiment with hash-grid encodings for the RF vector to compress and speed conditionals without hurting PSNR. (Paper references acknowledge Instant-NGP; integrating an RF head is natural.)
- Live loop: Bind to a streaming RF back-end and render RF-conditioned frames from moving sensors; keep the JSON→LaTeX exporter so runs remain auditable.
Takeaway
This project nails the Guangdong ethos: small kit, GPU where it matters, CPU when you must, and artifacts that explain themselves. If you can ship a renderer that plots its own Pareto and prints its own tables, you just made your ops team faster and your reviewers friendlier. Run the make target, pick your tradeoff, and deploy.
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