# Radiomap Compare Baselines

This directory stores external comparison baselines for the `radiomapvggt`
project.

Paths:
- code root: `/data/jing_code/wl/radiomap_compare`
- env root: `/data/conda_envs`
- dataset roots used for adaptation:
  - `/data/jing_dataset/processed/wireless/scannetpp`
  - `/data/jing_dataset/processed/3D/scannetpp`

## Paper fixed compare protocol

A fixed paper-facing comparison slice now lives under:
- [paper_fixed5_compare](/data/jing_code/wl/radiomap_compare/paper_fixed5_compare)

It defines:
- fixed `5` scenes
- fixed centered train/test `TX`
- fixed centered `z` order
- reusable scripts for:
  - `A06` fixed-case evaluation
  - cross-method metric tables

## Current status

### 1. NeRF2

- repo: [NeRF2](/data/jing_code/wl/radiomap_compare/NeRF2)
- upstream: `https://github.com/XPengZhao/NeRF2.git`
- local env: `/data/conda_envs/nerf2_compare`
- env status:
  - created
  - imports verified: `torch`, `yaml`, `imageio`, `pandas`, `skimage`, `einops`
- local notes:
  - [readme_compare.md](/data/jing_code/wl/radiomap_compare/NeRF2/readme_compare.md)
- current ScanNet++ adaptation status:
  - one real scene export validated:
    - `e0d1ed2159`
  - exported BLE-format dataset path:
    - `/data/jing_code/wl/radiomap_compare/NeRF2/data/scannetpp_ble/e0d1ed2159`
  - dataset load verified through upstream `BLE_dataset`
  - compare scripts/configs added under:
    - `/data/jing_code/wl/radiomap_compare/NeRF2/configs`
    - `/data/jing_code/wl/radiomap_compare/NeRF2/scripts`
  - current no-Slurm compare setup also supports:
    - `3` aligned scenes on `GPU 5-7`
    - automatic train/test postprocess plots

### 2. Second external baseline repo

### 2. RF-3DGS

- repo: [RF-3DGS](/data/jing_code/wl/radiomap_compare/RF-3DGS)
- upstream: `https://github.com/SunLab-UGA/RF-3DGS.git`
- local env: `/data/conda_envs/rf3dgs_compare`
- env status:
  - created as a dedicated clone of our known-good `gaussian_splatting` env
  - verified:
    - `torch`
    - `torchvision`
    - `diff_gaussian_rasterization`
    - repo entry `train.py -h`
- local notes:
  - [readme_compare.md](/data/jing_code/wl/radiomap_compare/RF-3DGS/readme_compare.md)
- current ScanNet++ adaptation status:
  - direct wireless-grid supervision is **not** plug-and-play
  - repo expects:
    - `images/`
    - `sparse/0/cameras.txt`
    - `sparse/0/images.txt`
    - `sparse/0/points3D.ply`
    - `train_index.txt`
    - `test_index.txt`
  - local exporters now cover both:
    - visual RGB path:
    - `/data/jing_code/wl/radiomap_compare/RF-3DGS/dataset_tools/export_scannetpp_visual_to_rf3dgs.py`
    - radiomap pseudo-image path:
    - `/data/jing_code/wl/radiomap_compare/RF-3DGS/dataset_tools/export_scannetpp_radiomap_to_rf3dgs.py`
  - current RF compare now has two modes:
    - visual-only scene fitting
    - radiomap pseudo-image fitting
  - current no-Slurm compare setup also supports a fixed aligned `5`-scene run on
    `GPU 0-4`, with automatic train/test rendering and overview plotting
  - training-length policy has been updated to match original 3DGS visual training:
    - default `iterations = 30000`

## Comparison strategy

These external baselines are not scene-generalization models like `radiomapvggt`.
They should be used as **single-scene fitting baselines**.

Recommended protocol:
- choose one scene from the canonical `small100 + val10` split
- train one baseline only on that scene
- use the same canonical wireless subset:
  - `27` TX from the centered `3x3x3` subset
  - `3` centered RX z-planes: `[4, 5, 6]`
- use the remaining scene-local unused data as held-out evaluation when the
  baseline structure allows it
- compare against `radiomapvggt` on the same scene

## Canonical split reference

Scene split and subset protocol are recorded at the dataset roots:
- [wireless README](/data/jing_dataset/processed/wireless/scannetpp/README.md)
- [3D README](/data/jing_dataset/processed/3D/scannetpp/README.md)

## Notes

- `NeRF2` already includes an internal non-neural baseline:
  - [mri.py](/data/jing_code/wl/radiomap_compare/NeRF2/baseline/mri.py)
  - [baseline readme_compare.md](/data/jing_code/wl/radiomap_compare/NeRF2/baseline/readme_compare.md)
- so even before a second external repo is added, we already have:
  - one neural baseline: `NeRF2`
  - one simple analytic baseline: `MRI`
- `RF-3DGS` is structurally different from both of the above:
  - stage 1 is standard 3DGS visual reconstruction from camera images
  - stage 2 expects RF supervision in an **image-like / spectrum-like** camera frame
  - our current processed wireless dataset is a TX/RX scalar-grid dataset, so RF-3DGS
    needs an explicit modality conversion layer instead of a direct loader swap
  - the new local radiomap path is exactly that bridge:
    - it exports radiomap slices as pseudo-images
    - it keeps the original 3DGS trainer unchanged
    - it should be interpreted as a forced single-scene fitting baseline