| # Golden MVS Data — process & provenance note |
|
|
| **For: a future Claude (or human) picking this up.** This explains *what* the |
| output is, *how* it was produced, and *how to use it*. It is grounded in the |
| actual run logs in `logs/` and `cuda_golden_data_task.md`. |
|
|
| --- |
|
|
| ## 1. What this is |
|
|
| The CUDA **golden dense-MVS reference** for the macOS/Metal PatchMatch port |
| (Task 1 of `cuda_golden_data_task.md`). It is COLMAP's CUDA `patch_match_stereo` |
| output on the **`south-building`** scene (128 images), to be diffed against a |
| future Metal `patch_match_stereo` port within tolerance. |
|
|
| The MVS CUDA code (`patch_match_cuda.cu`) is identical to upstream, so this |
| upstream-COLMAP output is the correct reference. |
|
|
| ## 2. Environment it was generated on |
|
|
| | Resource | Value | |
| |---|---| |
| | Platform | Google Colab | |
| | GPU | **NVIDIA Tesla T4**, 15 GB, 70 W cap, CUDA arch `75` | |
| | CPU | Intel Xeon @ 2.00 GHz, **2 vCPU** (1 core × 2 threads) | |
| | RAM | **12 GiB** | |
| | COLMAP | 4.0.4 (built with CUDA), `/usr/local/bin/colmap` | |
| | pycolmap | 4.0.4 (for reading depth maps) | |
|
|
| ## 3. The pipeline (exactly what ran) |
|
|
| Standard, documented COLMAP dense CLI — see `colmap_cuda_golden_data.ipynb`: |
|
|
| ``` |
| image_undistorter # sparse SfM + images -> dense workspace (cap 2000 px) |
| ↓ |
| patch_match_stereo # CUDA dense MVS, geom_consistency=true (the GPU step) |
| ↓ --PatchMatchStereo.geom_consistency true (NO max_image_size: runs at |
| ↓ undistorted resolution = default -1) |
| stereo_fusion # geometric depth/normal maps -> fused.ply |
| --input_type geometric |
| ``` |
|
|
| The notebook is **idempotent**: each cell skips when its output already exists, |
| so it survives a Colab disconnect and can be re-run cheaply. |
|
|
| ### Notes / gotchas baked into the notebook |
| - **Fusion bug (fixed).** An earlier template chained `stereo_fusion` into |
| `poisson_mesher` with a trailing `\`, giving `bash: line 4: : command not |
| found` and no `fused.ply`. Each CLI call is now its own statement. |
| - **Resolution.** `patch_match_stereo` is run *without* `--max_image_size` (COLMAP |
| default −1) to match how this golden set was produced. Undistortion already |
| caps images at `UNDISTORT_MAX_IMAGE_SIZE = 2000`, so memory stays bounded. |
| - The run here resumed an in-progress `patch_match_stereo`; the notebook's skip |
| guards then completed fusion and packaging. |
|
|
| ## 4. Outputs (this bundle) |
|
|
| ``` |
| colmap_cuda_golden_data.ipynb # the executed, idempotent notebook |
| note.md # this file |
| golden_mvs/dense/ |
| fused.ply # 93 MB, 3,609,743 fused points |
| stereo/depth_maps/ *.geometric.bin # 128 geometric depth maps (the reference) |
| stereo/normal_maps/ *.geometric.bin # 128 geometric normal maps |
| logs/ # undistort / patch_match / fusion logs |
| ``` |
| (The separate `_full.zip` also holds the 128 `*.photometric.bin` maps and |
| consistency graphs; usually not needed.) |
|
|
| ### Depth/normal map format — how to read them |
| COLMAP dense binary map: ASCII header `width&height&channels&` then |
| little-endian `float32` in **Fortran (column-major)** order. Reader (matches |
| COLMAP `scripts/python/read_write_dense.py::read_array`, see notebook cell 8): |
|
|
| ```python |
| import numpy as np |
| def read_colmap_array(path): |
| with open(path, "rb") as fid: |
| hdr, amp = b"", 0 |
| while amp < 3: |
| ch = fid.read(1); hdr += ch |
| if ch == b"&": amp += 1 |
| w, h, c = (int(x) for x in hdr.decode().split("&")[:3]) |
| data = np.fromfile(fid, np.float32) |
| return np.transpose(data.reshape((w, h, c), order="F"), (1, 0, 2)).squeeze() |
| ``` |
| Depth maps are `H×W` float32; **0 = invalid** (ignore zeros). Normal maps are |
| `H×W×3`. Sanity sample (`P1180141.JPG.geometric.bin`): shape `1196×1600`, |
| 56.3 % valid, depth range `0.880 .. 8.956`. |
|
|
| ## 5. How to use on the Mac |
| Drop under `golden_task/golden_mvs/south-building/`. The future Metal MVS |
| validation compares its `patch_match_stereo` depth/normal maps against these |
| **on overlapping valid pixels (ignore zeros)**, within tolerance. CUDA output is |
| itself an approximation — for *accuracy* (vs truth) prefer ETH3D/DTU real GT |
| (Task 3); use these golden maps as the "did I port the *same* algorithm |
| faithfully" diff. |
|
|
| ## 6. Performance profile (measured from `logs/`) |
|
|
| Wall-clock for the full one-time generation on the T4 box: |
|
|
| | Stage | Wall time | Bound by | Notes | |
| |---|---|---|---| |
| | `image_undistorter` | ~10 min | CPU (2 vCPU) | 128 images → dense workspace | |
| | `patch_match_stereo` | **~86 min** (11:25:52 → 12:52:12) | **GPU** | 128 views, photometric + geometric passes, 5 iterations each; ~1.0–1.4 s per CUDA sweep | |
| | `stereo_fusion` | ~5.8 min | CPU (single thread) | 3,609,743 points | |
| | packaging (zip) | ~6 min | CPU + I/O | 4.7 GB full archive | |
| | **Total** | **≈ 1 h 48 m** | — | one-time per session | |
|
|
| Peak resource usage **during `patch_match_stereo`** (observed via `nvidia-smi` |
| / `ps`): |
|
|
| | Resource | Peak | Of budget | Comment | |
| |---|---|---|---| |
| | GPU utilization | **100 %** | T4 | fully GPU-bound — the step the Mac cannot run | |
| | GPU memory | ~0.9 GB | / 15 GB | small; bounded by the 2000 px undistort cap | |
| | CPU | ~99 % of **1** thread | / 2 vCPU | host side single-threaded per view | |
| | RAM (RSS) | ~5.6 GB | / 12 GiB | comfortably within RAM | |
| | Disk | ~7.4 GB raw maps | / 236 GB | depth 1.9 GB + normals 5.5 GB; fused 93 MB | |
|
|
| **Takeaways for re-running / scaling:** |
| - The job is **GPU-bound**; a faster/larger GPU (e.g. A100) is the main lever. |
| More vCPUs mainly speed undistort + fusion + zip, not the MVS step. |
| - T4 memory is *not* the bottleneck here (0.9 GB used). Raising |
| `UNDISTORT_MAX_IMAGE_SIZE` increases both VRAM and time roughly with pixel |
| count — the default 2000 keeps a 128-image scene to ~1.5 h. |
| - Normal maps dominate disk (5.5 GB); the slim geometric reference is what the |
| Mac side actually needs. |
|
|
