Remove dev data from clean release repo

Moved 3d-belief data bundle and ARKitScenes files to SCAI-JHU/3d-belief-dev; remove them from the clean release repo.

3d-belief/ARKITSCENES.md

@@ -1,263 +0,0 @@
-# ARKitScenes → 3D-Belief finetuning
-
-End-to-end procedure for wiring **ARKitScenes** (Apple iPad LiDAR room captures)
-into 3D-Belief training via the CUT3R adapter, plus the single-scene overfit used
-to validate the dataloader before multi-scene training.
-
-This mirrors the WildRGBD / ScanNet++ ports — see [`WILDRGBD.md`](WILDRGBD.md) and
-the `3d-belief-dataloader` skill for the shared invariants. Only the
-ARKitScenes-specific decisions are spelled out here.
-
-TL;DR of what was added:
-
-| file | change |
-|---|---|
-| `splat_belief/data_io/cut3r_adapter.py` | `_make_arkitscenes_short()` helper + `arkitscenes` branch now uses it |
-| `splat_belief/data_io/__init__.py` | registered `arkitscenes_cut3r` in `get_path` + the adapter tuple |
-| `splat_belief/config/dataset/arkitscenes_cut3r.yaml` | new Hydra dataset config (room-scale near/far, metric depth) |
-| `scripts/data_prep/verify_arkitscenes_pose_convention.py` | convention + schema + drift verifier |
-| `scripts/training/finetune_arkitscenes_overfit.sh` | single-scene overfit launcher (DFoT init, depth mask on) |
-
----
-
-## 1. Preprocessing (CUT3R)
-
-ARKitScenes uses the **two-step** CUT3R pipeline (`docs/preprocess.md`), identical
-in shape to ScanNet: `preprocess_*` writes per-frame RGB/depth + `scene_metadata.npz`
-(with DUSt3R `pairs`); `generate_set_*` then writes `new_scene_metadata.npz` (with
-the `image_collection` / `video_collection` that the `ARKitScenes_Multi` loader
-actually reads at train time).
-
-### 1a. Inputs
-
-- **Raw data**: `data/ARKitScenes/raw/{Training,Validation}/<scene>/` with
-  `vga_wide/` (RGB), `lowres_depth/` (uint16 mm LiDAR depth), `vga_wide_intrinsics/`
-  (`*.pincam`), and `lowres_wide.traj` (per-frame device pose, angle-axis + t).
-- **Precomputed pairs** (required by `preprocess_arkitscenes.py`): DUSt3R's
-  [`arkitscenes_pairs.zip`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/arkitscenes_pairs.zip),
-  unzipped to `data/ARKitScenes/arkitscenes_pairs/{Training,Test}/`. The script
-  reads `scene_list.json` + per-scene `selected_pairs.npz`; **without it the
-  preprocessor cannot run** (it drives which frames are selected/exported).
-
-### 1b. Commands (run from `third_party/CUT3R/datasets_preprocess`)
-
-Use the `3d-belief-release` conda env's python for preprocessing — it has
-`numpy<2` + `numpy-quaternion` (the `3d-belief` training env has numpy 2.x where
-`quaternion` is ABI-broken; that's fine because the **dataloader** path never
-imports quaternion). Use the **absolute** interpreter path; `conda run -n <env>`
-is broken on this host (it silently resolves to the colmap-cuda env's py3.14).
-
-```bash
-PY=/home/ubuntu/tianmin-neurips/miniconda3/envs/3d-belief-release/bin/python
-DATA=/home/ubuntu/tianmin-neurips/zwen19/data/ARKitScenes
-
-# Step 1 — convert RGB/depth + interpolate poses to selected timestamps.
-$PY preprocess_arkitscenes.py \
-    --arkitscenes_dir   "$DATA/raw" \
-    --precomputed_pairs "$DATA/arkitscenes_pairs" \
-    --output_dir        "$DATA/processed_arkitscenes"
-
-# Step 2 — build image_collection / video_collection metadata.
-$PY generate_set_arkitscenes.py \
-    --root "$DATA/processed_arkitscenes" \
-    --splits Training Test --max_interval 5.0 --num_workers 8
-```
-
-Output layout (per scene): `processed_arkitscenes/Training/<scene>/{vga_wide/*.jpg,
-lowres_depth/*.png, scene_metadata.npz, new_scene_metadata.npz}` plus a top-level
-`all_metadata.npz` + `scene_list.json` per split.
-
-### 1c. Subset preprocessing (this run)
-
-The full ARKitScenes is still downloading, so only a **5-scene subset** was
-processed for the overfit smoke. The official `preprocess_arkitscenes.py`
-hardcodes `subdirs=["Test","Training"]` and crashes on an empty split
-(`np.concatenate([])`), and iterates the *entire* pairs `scene_list.json`. To
-process just a subset without touching the original script:
-
-1. Build `arkitscenes_pairs_subset/` with a filtered `Training/scene_list.json`
-   (chosen scenes), symlinked `Training/<scene>/selected_pairs.npz`, and an empty
-   `Test/scene_list.json` (`[]`).
-2. Run a `/tmp` copy of `preprocess_arkitscenes.py` patched to `subdirs=["Training"]`
-   (avoids the empty-Test crash; the original CUT3R script is left intact).
-3. Run `generate_set_arkitscenes.py --splits Training` on the output.
-
-Valid processed scenes: **`40958756`** (162 selected frames — overfit target),
-`41045408`, `41098145`. (Re-run on the full pairs `scene_list` once the download
-completes; no code changes needed.)
-
----
-
-## 2. Dataloader wiring
-
-### 2a. `_make_arkitscenes_short()` — the ARKitScenes-specific subclass
-
-The raw CUT3R `ARKitScenes_Multi` is **not** directly usable for 3D-Belief; the
-helper returns a subclass fixing three things (full rationale in the docstring):
-
-1. **`<ROOT>_highres` crash.** `_load_data` does
-   `np.setdiff1d(scenes, os.listdir(ROOT + "_highres"/split))` to drop scenes that
-   also exist in the high-res variant. We only process the regular split, so the
-   subclass stubs empty `<ROOT>_highres/{Training,Validation}` dirs before
-   `_load_data` runs → the setdiff is a no-op.
-2. **50/50 shuffled sampling.** `_get_views` flips `rng.choice([True,False])`
-   between a temporal-video sequence and a *permuted* DUSt3R-pairs group. The pairs
-   branch is wide-baseline + non-temporal, which breaks the `[ctxt, intm…, trgt]`
-   time ordering 3D-Belief's intermediate loss requires. A tiny `_ForceVideoRng`
-   proxy makes the branch selector always return the video branch, forwarding every
-   other rng call unchanged.
-3. **Pose drift too wide.** Default `max_interval=8` + `video_prob=0.8` give
-   multi-frame gaps. The subclass forces `video_prob=1.0` (always time-ordered) and
-   tightens `max_interval=2`, matching `_make_scannet_short`.
-
-### 2b. Hydra config (`arkitscenes_cut3r.yaml`)
-
-Room-scale handheld capture, so it differs from object-centric WildRGBD:
-
-| key | value | why |
-|---|---|---|
-| `cut3r_dataset_class` | `arkitscenes` | routes to `_make_arkitscenes_short` |
-| `viz_type` | `interpolation` | ctxt/trgt are close after short-window sampling → linear interp stays in seen views (WildRGBD needed `spherical` because object-centric) |
-| `near` / `far` | **0.1 / 10.0** | room-scale indoor; covers full room while keeping splat precision tighter than the 20 m RE10K/ScanNet default |
-| `depth_loss_weight` | 1.0 | per-frame **metric** LiDAR depth on every pixel |
-| `depth_smooth_loss_weight` | 0.1 | canonical (regularizes predicted depth) |
-| `intermediate_weight` | 5.0 | canonical (train.sh / train_re10k.sh) |
-| `vggt_alignment_loss_weight` | 2.0 | canonical |
-| `camera.fx`/`fy` | 1.16 | **measured** fx_norm after the square center-crop (HFOV ≈ 47°); cx/cy ≈ 0.5 confirms centered crop. Nominal fallback only — per-sample K from the dataset dict takes precedence. |
-
----
-
-## 3. Camera convention — VERIFIED OpenCV c2w (no flip)
-
-This was the make-or-break check. ARKit raw poses are OpenGL-ish, and CUT3R's
-preprocessor applies a non-trivial `pose_cam_to_world @ rotated_to_cam` (an
-in-plane sky-direction rotation), so the output convention was **not assumed** —
-it was tested on real geometry by
-`scripts/data_prep/verify_arkitscenes_pose_convention.py`.
-
-**Test 1 — camera +Y world axis (Z-up world).** Mean camera +Y → world
-`(-0.08, -0.12, -0.99)` = dominant **−Z** across all views ⇒ camera-Y points down
-⇒ OpenCV (y-down). (OpenGL y-up would give +Z.)
-
-**Test 2 — multi-view point-cloud consistency** (the definitive test). Backproject
-two **adjacent, high-overlap** frames into the world under OpenCV (c2w as-is) vs
-OpenGL (`c2w @ diag(1,-1,-1,1)`) and measure cross-frame overlap:
-
-| frame pair | OpenCV as-is | OpenGL flip |
-|---|---|---|
-| (0,1), baseline 2.9 cm | **median 0.011 m · 94% < 5 cm** | 0.016 m · 74% |
-| (0,2), baseline 6.3 cm | **0.016 m · 93%** | 0.026 m · 62% |
-| (10,11), rotated | **0.008 m · 94%** | 0.047 m · 53% |
-
-OpenCV wins every pair, and the margin **grows with inter-frame rotation** (the
-(10,11) pair: 94% vs 53%) — conclusive. (Comparing ctxt vs trgt directly is
-unreliable: they barely overlap, which made an earlier 128-res ctxt/trgt test
-ambiguous. Always use adjacent frames for this check.)
-
-**Conclusion: CUT3R-preprocessed ARKitScenes `camera_pose` is c2w OpenCV.** The
-`Cut3rAdapter` consumes it unchanged — **no `diag(1,-1,-1,1)` flip** (unlike SPOC's
-Habitat poses). Adapter schema checks also pass: `ctxt_c2w[0]==identity`, RGB in
-`[-1,1]`, depth 100% valid, normalized `cx≈cy≈0.5`.
-
-**Pose drift** (40 samples, `max_interval=2`): mean translation **0.14 m**, mean
-rotation **23°** — comfortably inside SPOC's bounds (1.0 m / 45°). `max_interval=2`
-is a good default; raise it only if the full dataset's selected frames turn out to
-be more densely spaced in time.
-
----
-
-## 4. Finetuning / overfit
-
-**Init: DFoT weights**, exactly as train.sh / train_re10k.sh / the WildRGBD
-overfit — `checkpoint_path=checkpoints/DFoT_RE10K.ckpt` (diffusion init, full model)
-+ `model.encoder.encoder_ckpt=checkpoints/re10k.ckpt` (pixelsplat encoder).
-`load_enc=true`, `load_optimizer=false`. The ~514 missing / 84 unexpected keys on
-load are **expected** (DFoT init has a different head than the re10k encoder).
-
-**Env: use the `3d-belief` conda env (torch 2.11), NOT `3d-belief-release`** — the
-release env's `diff_gaussian_rasterization/_C.so` is stale (`undefined symbol
-…decref_pyobject`). The `3d-belief` env has a working rasterizer build.
-
-**Depth mask: ENABLED** (the SPOC-style setup the user asked for):
-`use_depth_supervision=true`, `model.encoder.use_depth_mask=true`. The adapter's
-`(d>0)&(d<far)` mask excludes invalid (0) LiDAR pixels.
-
-Launch the single-scene overfit (scene `40958756`, `overfit_to_index=0`):
-
-```bash
-cd /home/ubuntu/tianmin-neurips/zwen19/3d-belief
-TRAIN_NUM_STEPS=8000 SAMPLE_EVERY=2000 SAVE_EVERY=4000 \
-WARMUP_PERIOD=200 LR=2e-5 CUDA_VISIBLE_DEVICES=3 \
-RESULTS_FOLDER=outputs/training/arkitscenes_overfit_scene0 \
-  bash scripts/training/finetune_arkitscenes_overfit.sh
-```
-
-### Key hyperparameters (overfit smoke)
-
-| param | value | note |
-|---|---|---|
-| `image_size` | 128 | DFoT_RE10K / re10k.ckpt native res |
-| `overfit_to_index` | 0 | deterministic (factory sets `cut3r_seed=42`) → scene 40958756, group 0 |
-| `num_context` / `num_target` | 1 / 1 | forced by `setting_name=debug` regardless of CLI |
-| `num_intermediate` | 3 | `intermediate=true` |
-| `warmup_period` | **200** | base config default is **10000** — that keeps LR≈0 for a short overfit (lr 2e-5 ⇒ +2e-9/step). Multi-scene training keeps 10000. |
-| `lr` | 2e-5 | canonical |
-| depth | `use_depth_supervision=true`, `use_depth_mask=true` | |
-| losses | rgb 1.0, depth 1.0, depth_smooth 0.1, intermediate 5.0, vggt_alignment 2.0, repa 5.0, lpips 1.0 | from `arkitscenes_cut3r.yaml` |
-
-### Smoke result (120-step, default warmup)
-
-Pipeline runs end-to-end from DFoT with no errors; per-component losses all
-present (`rgb_loss`, `depth_loss`≈1.8, `alignment_loss`≈4.96, no NaNs), depth
-supervision active, `rgb_loss_ctxt` already trending down (0.74 → 0.37). 120 steps
-under the 10000-step warmup is not a convergence test — see the 8000-step run
-(`outputs/training/arkitscenes_overfit_scene0/logs.json.txt`) with `warmup_period=200`
-for the actual overfit curve.
-
-### Overfit result (8000-step, `warmup_period=200`, scene 40958756, GPU 3)
-
-Single-scene overfit **converges cleanly and monotonically** (100-step means):
-
-| step | total loss | rgb_loss | rgb_loss_ctxt | depth_loss | alignment_loss |
-|---|---|---|---|---|---|
-| 0–100 | 39.9 | 0.461 | 0.445 | 1.760 | 4.953 |
-| 200–300 | 17.4 | 0.069 | 0.050 | 0.277 | 4.608 |
-| 500–600 | 11.5 | 0.036 | 0.041 | 0.079 | 3.718 |
-| 1000–1100 | 8.4 | 0.019 | 0.035 | 0.056 | 3.095 |
-| 1500–1600 | 7.1 | 0.014 | 0.031 | 0.050 | 2.782 |
-| 2000–2100 | **6.3** | **0.011** | **0.028** | **0.049** | 2.543 |
-
-`rgb_loss` drops below the skill's **0.05 overfit threshold by ~step 250** and reaches
-**0.011 by step 2000**; `depth_loss` 1.8 → 0.049 (depth-mask supervision is fitting the
-LiDAR depth); VGGT `alignment_loss` 5.0 → 2.5. **Milestone renders are healthy** — the
-`milestone_1_rendered_frames/rendered_{0..19}.png` camera sweep has **0.0–0.2% black
-pixels** across all frames (skill threshold: ≤1% healthy, >5% = collapsed gaussians), and
-`rendered_10.png` (middle of the sweep) shows a coherent room (window+blinds, furniture),
-not black. This is the definitive confirmation the dataloader + OpenCV convention are
-correct — a wrong pose convention or pose-drift bug would empty the middle frames and
-stall `rgb_loss` above 0.05.
-
-Verdict: **a single ARKitScenes scene overfits**, so 3D-Belief can be trained on it. Scale
-to multi-scene once the full dataset finishes downloading (re-run §1 preprocessing on the
-full pairs `scene_list`, drop `overfit_to_index`, restore `warmup_period=10000`).
-
----
-
-## 5. Gotchas (this host / dataset)
-
-- **`conda run -n <env>` is broken here** — silently resolves to the colmap-cuda
-  env (py3.14). Always use absolute interpreter paths
-  (`.../envs/<env>/bin/python`).
-- **Two different envs**: preprocessing → `3d-belief-release` (numpy<2 +
-  quaternion); training → `3d-belief` (torch 2.11, working rasterizer).
-- **Precomputed pairs are mandatory** for `preprocess_arkitscenes.py`; it won't run
-  without DUSt3R's `arkitscenes_pairs`.
-- **`generate_set` is not optional** — the loader reads `new_scene_metadata.npz`
-  (image_collection), which only step 2 writes. Step 1 alone → loader finds no
-  groups.
-- Don't lower `video_prob` back below 1.0 or widen `max_interval` past ~2 for
-  handheld capture — both reintroduce the wide-baseline / non-temporal sampling
-  that empties the gaussian middles.
-- See [`scripts/data_prep/verify_arkitscenes_pose_convention.py`] — re-run the
-  convention check before any new ARKitScenes training; trusting the upstream c2w
-  without verification is the #1 way to get silent garbage.

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