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README.md

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+---
+task_categories:
+- other
+---
+
+# 4DCodeBench-ModelEstimation
+
+[Project page](https://4dcodebench.com) | [Paper](https://huggingface.co/papers/2610.03715) | [Code](https://github.com/4DCodeBench/4DCodeBench)
+
+The depth, optical flow and point tracks that [4DCodeBench](https://4dcodebench.com) scores reconstructions against, so the benchmark can be evaluated without installing the models behind them. On the 100 real-world cases they are estimated from the reference video by off-the-shelf models and released here as files. On the 100 synthetic cases they are ground truth, computed from the reference world by the script in `scripts/`.
+
+## Layout
+
+```text
+real/<case>/depth.h5        Video Depth Anything disparity
+real/<case>/flow.h5         RAFT optical flow
+real/<case>/tracks.npz      CoTracker3 point tracks
+real/<case>/moge.npz        MoGe-3 point map of frame 0
+scripts/extract_synthetic.py
+```
+
+53 GB: 46.1 GB of flow, 5.6 GB of depth, 0.9 GB of point maps and 0.2 GB of tracks. `<case>` is the case name of [Dataset-Real-World](https://huggingface.co/datasets/4DCodeBench/Dataset-Real-World).
+
+## Real-world cases
+
+Each file was computed once from the case's `reference.mp4` and is what every score in the paper was read against. `F`, `H` and `W` are the video's frame count, height and width; every real video has at most 300 frames, so all of them are read frame by frame (`stride` is 1).
+
+| file | model | contents |
+|---|---|---|
+| `depth.h5` | [Video Depth Anything](https://huggingface.co/depth-anything/Video-Depth-Anything-Large), Large, FP32 | `disparity (F, h, w)` float16, relative (affine-invariant), on the model's own output grid (short side 518 px on 98 of the 100 cases), to be resized to `(H, W)`; `shape [F, H, W]`, `fps`, `stride` |
+| `flow.h5` | [RAFT](https://pytorch.org/vision/stable/models/raft.html), torchvision `Raft_Large_Weights.C_T_SKHT_V2`, 12 iterations | `flow (F-1, H, W, 2)` float16, `(u, v)` pixels from frame `t` to `t + 1`; `valid (F-1, H, W)` bool, all true; `stride`, `model`, `iterations` |
+| `tracks.npz` | [CoTracker3](https://huggingface.co/facebook/cotracker3), offline | `tracks (Q, F, 2)` float32 pixels, `visible (Q, F)` bool, `queries (Q, 3)` `(0, x, y)` on frame 0, `on_mask (Q,)` bool, `shape [F, H, W]`, `step`, `stride`, `model`, `tracking_resolution` |
+| `moge.npz` | [MoGe-3](https://huggingface.co/Ruicheng/moge-3-vitl), ViT-L, resolution level 9, 3 refinement steps | `points (H, W, 3)` float32 in camera space up to the model's scale, `mask (H, W)` bool, `intrinsics (3, 3)` normalised to the unit image, `shape [H, W]`, `model` |
+
+The `.h5` files are HDF5 with LZF compression, one frame per chunk. Flow, tracks and points are in full-resolution pixels of the video.
+
+The track queries are the benchmark's grid of at most 2048 points on frame 0: 512 spread over the whole frame, which Track2D uses to remove camera motion, and the rest laid densely inside the case's annotated dynamic mask, marked by `on_mask`. CoTracker3 tracks the video at a long side of 768 px (`tracking_resolution`) and the tracks are scaled back to full resolution.
+
+23 real videos cannot be redistributed and are rebuilt by Dataset-Real-World's `prepare_videos.py`, whose output can slightly differ from the benchmark's copy by H.264 encoder noise. The estimates here are of the benchmark's copy.
+
+## Synthetic cases
+
+A synthetic case has the world it was rendered from, so its depth, flow and tracks are computed exactly rather than estimated. `scripts/extract_synthetic.py` writes them in the same layout as the real cases, from the meshes, material trajectories and camera of each world, with the same code the scorer uses to read a submission. No model checkpoint is needed.
+
+With the [code release](https://github.com/4DCodeBench/4DCodeBench) set up and the synthetic worlds downloaded by its `scripts/download_data.py --kind synthetic`, run from the code release's root:
+
+```bash
+python /path/to/scripts/extract_synthetic.py              # all 100 cases into ./synthetic/<case>/
+python /path/to/scripts/extract_synthetic.py B_01 --out gt
+```
+
+It needs a CUDA GPU for nvdiffrast; a case of 100–150 frames takes about 15 s and the longest, 600 frames, under a minute.
+
+| file | contents |
+|---|---|
+| `depth.h5` | `depth (F', H, W)` float32 camera-space z in metres, `inf` where no surface covers the pixel; `shape [F, H, W]`, `fps`, `stride` |
+| `flow.h5` | `flow (F'-1, H, W, 2)` float16 pixels from sampled frame `k` to `k + 1`, `valid (F'-1, H, W)` bool, `stride` |
+| `tracks.npz` | the keys of the real cases but `tracking_resolution`, with the dense queries on the world's own frame-0 dynamic pixels and `model` `ground_truth` |
+
+The 8 synthetic videos longer than 300 frames are read on a thinned timeline: frames `0, s, 2s, …` with the smallest stride `s` that keeps at most 300 of them, so `F' = ceil(F / s)`. `stride` in each file records `s`.
+
+## Using them with the benchmark
+
+The code release reads these files from `data/real/<case>/estimates/` under the same names, so copying `real/<case>/*` there lets `python -m scorer` read Flow, Track2D, Depth error and Uni3D MoGe without the estimation models. Its other reference features (DINOv3, TIPS) still come from `python -m scorer.prepare --only semantic,geophys`, whose models are on the Hugging Face Hub.
+
+## Licences
+
+The real-world files are outputs of models with their own licences: Video Depth Anything Large and CoTracker3 are CC BY-NC 4.0, RAFT (torchvision) is BSD-3-Clause, and MoGe-3 is MIT. The videos they were computed from are under the terms listed in Dataset-Real-World.

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scripts/extract_synthetic.py

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+#!/usr/bin/env python3
+"""Ground-truth depth, flow and tracks of the synthetic cases, from their reference worlds.
+
+Run from the root of the 4DCodeBench code release, after its environment is set up
+and `python scripts/download_data.py --kind synthetic` has unpacked the worlds:
+
+    python /path/to/extract_synthetic.py                         # every case under data/synthetic
+    python /path/to/extract_synthetic.py B_01 G_03 --out gt      # some cases, another output root
+
+Each case gets `<out>/<case>/` with the same files, keys and timeline as the real
+cases' estimates, computed from the world's meshes and camera instead of a model:
+
+    depth.h5     `depth (F', H, W)` float32 camera-space z in metres, `inf` where no
+                 surface covers the pixel; `shape [F, H, W]`, `stride`, `fps`
+    flow.h5      `flow (F'-1, H, W, 2)` float16 pixels from sampled frame k to k + 1,
+                 `valid (F'-1, H, W)` bool, `stride`
+    tracks.npz   `tracks (Q, F', 2)` float32 pixels, `visible (Q, F')` bool, the frame-0
+                 queries `queries (Q, 3)` `(0, x, y)`, `on_mask`, `shape`, `step`, `stride`
+
+Flow and tracks follow each surface point through the world: by its face and
+barycentrics where the object keeps its topology, by the nearest material columns of
+`dynamics/` where it does not (a liquid, a fracture). These are the scorer's own
+`scorer.analytic` paths, the ones it reads every submission with. The query grid is
+the benchmark's: dense on the frame-0 dynamic pixels, sparse over the whole frame.
+Files already written are skipped, so an interrupted run resumes by running again.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__,
+                                 formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("cases", nargs="*", help="case names (default: every world under --data)")
+    ap.add_argument("--code", type=Path, default=Path("."), help="code release root (default: .)")
+    ap.add_argument("--data", type=Path, default=None,
+                    help="directory of <case>/world (default: <code>/data/synthetic)")
+    ap.add_argument("--out", type=Path, default=Path("synthetic"), help="output root")
+    args = ap.parse_args()
+
+    sys.path.insert(0, str(args.code.resolve()))
+    from config.estimates import DEPTH_FILENAME, FLOW_FILENAME, TRACKS_FILENAME
+    from config.sampling import sampled_indices, stride_for
+    from scorer.analytic.flow import frame_flow
+    from scorer.analytic.tracks import DRIFT, grid_queries, grid_step, inside, track_points
+    from scorer.raster import default_device, id_frame, rasterize_frame
+    from scorer.storage import write_dense
+    from scorer.world import World
+
+    data = args.data or args.code / "data/synthetic"
+    cases = args.cases or sorted(p.parent.name for p in data.glob("*/world"))
+    device = default_device()
+
+    for number, case in enumerate(cases, 1):
+        out = args.out / case
+        out.mkdir(parents=True, exist_ok=True)
+        world = World(data / case / "world")
+        stride = stride_for(world.frames)
+        frames = sampled_indices(world.frames, stride)
+        height, width = world.shape[1:]
+        started = time.time()
+
+        if not (out / DEPTH_FILENAME).exists():
+            depth = np.stack([rasterize_frame(world, int(t), device)[0] for t in frames])
+            write_dense(out / DEPTH_FILENAME, depth=depth.astype(np.float32),
+                        shape=np.asarray(world.shape, dtype=np.int64),
+                        stride=np.int64(stride), fps=np.float64(world.fps))
+
+        if not (out / FLOW_FILENAME).exists():
+            flows, valids = [], []
+            for t in frames[:-1]:
+                flow, valid, _ = frame_flow(world, int(t), device, stride=stride)
+                flows.append(flow.cpu().numpy().astype(np.float16))
+                valids.append(valid.cpu().numpy())
+            write_dense(out / FLOW_FILENAME, flow=np.stack(flows), valid=np.stack(valids),
+                        stride=np.int64(stride), model="ground_truth")
+
+        if not (out / TRACKS_FILENAME).exists():
+            mask = np.isin(id_frame(world, 0, device), world.dynamic_ids)
+            mask = mask if mask.any() else None
+            queries = grid_queries(height, width, mask=mask)
+            on_mask = (inside(queries[:, 1:], mask) if mask is not None
+                       else np.zeros(len(queries), dtype=bool))
+            step = (grid_step(height, width, max(len(queries) - DRIFT, 1), mask)
+                    if mask is not None else grid_step(height, width))
+            tracks, visible = track_points(world, queries, device, stride=stride)
+            np.savez_compressed(out / TRACKS_FILENAME, tracks=tracks.astype(np.float32),
+                                visible=visible, queries=queries.astype(np.float32),
+                                on_mask=on_mask,
+                                shape=np.asarray([len(frames), height, width], dtype=np.int64),
+                                step=np.int64(step), stride=np.int64(stride),
+                                model="ground_truth")
+
+        print(f"[{number}/{len(cases)}] {case}: {len(frames)} frames, "
+              f"{time.time() - started:.0f}s", flush=True)
+
+
+if __name__ == "__main__":
+    main()