matting8k v1: 24 scenes 8K

.gitattributes

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bench/matting8k_v1/README.md

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+# matting8k 벤치마크 v1 (8K, 24장면)
+
+Blender Cycles로 렌더한 합성 matting 벤치마크다. 입력 이미지와 GT α를 같은 장면에서 렌더 패스를 조합해 계산한다. 사람이 칠한 GT가 아니다.
+
+- **GT의 정확성:** 모든 장면이 렌더 검증을 통과했다 (`index.json`에는 all_pass 장면만 들어 있다).
+  - α와 F, B는 입력 I에 대해 I = αF + (1−α)B를 정확히 만족한다.
+  - 검증: 완전 전경 픽셀에서 I = Tb, 완전 배경 픽셀에서 I = B가 성립한다 (p99 오차 ≤ 1e-6 수준).
+- **v1의 범위:** 24장면, 물체별 α 50개다 (L1 6장면: 식물 2, 오브젝트 2, 털 1, 그물 1 / L2·L3·L4 각 6장면).
+  - v0(12장면)와 Poly Haven 에셋이 하나도 겹치지 않고, v1 안에서도 같은 에셋을 두 번 쓰지 않았다. 털과 그물은 절차적으로 매번 새로 만든다.
+  - v0와 합치면 36장면이다. 두 폴더는 형식이 같아서 같은 `evaluate.py`로 각각 평가하면 된다.
+  - 기준 해상도는 8K(7680×4320)다. 에셋 텍스처가 8K를 받쳐주지 못하는 장면은 4K로 렌더했다 (24장면 중 2장면) (`index.json`의 `native_resolution`).
+  - 모든 장면에 1/2, 1/4 해상도 버전이 함께 있다. 정확한 premultiplied 다운샘플이라 GT도 그대로 정확하다.
+  - 렌더 품질은 256 spp다. 최종본은 spp를 올릴 예정이다.
+
+## 트랙
+
+| 트랙 | 난이도 | 내용 | GT |
+|---|---|---|---|
+| `single` | L1 | 전경 물체 1개 (식물, 오브젝트, 방충망) | `alpha.png` |
+| `multi` | L2/L3/L4 | 물체 2~3개 | 물체마다 보이는 α: `instances/alpha_<k>.png` |
+
+multi의 난이도:
+- **L2:** 물체들이 화면에서 떨어져 있다.
+- **L3:** 화면에서 겹친다. 물체 표면을 화면에 투영한 실루엣 겹침이 15~50%가 되도록 배치했다 (실측 16~56%, v0는 bbox 기준).
+- **L4:** L3에 0.8~3 m 깊이차와 얕은 DoF가 더해지고, 물체 하나에만 초점이 맞는다.
+
+물체별 α는 그 물체가 **실제로 보이는** coverage다. 앞 물체에 가려진 만큼은 빠지고, 모든 물체의 α를 더하면 전체 α와 정확히 같다.
+- 물체를 지정하는 입력으로 `instances/guide_<k>.png`(α_k > 0.5인 이진 마스크)와 `instances/trimap_<k>.png`를 제공한다.
+- 모델이 받는 guidance 방식에 맞게 골라 쓰면 된다.
+
+## 폴더 구조
+
+```
+index.json                          장면 목록과 메타데이터 (카테고리, 난이도, 카메라, 배경, unknown 비율 ...)
+evaluate.py                         평가 스크립트 (numpy, scipy, pillow만 필요)
+scenes/<sid>/<W>/image.png          입력 (8-bit sRGB)
+                 image16.png        입력 (16-bit sRGB, 같은 이미지)
+                 alpha.png          GT α (16-bit)                       [single]
+                 trimap_narrow.png  trimap 0/128/255. 폭은 alphamatting의 22 px @ 3000 px 폭을 해상도에 비례해 조정 [single]
+                 trimap_wide.png    같은 방식, 44 px @ 3000 px          [single]
+                 ignore.png         255 = 평가 제외 (굴절 유리, 아래 참고)
+                 instances/alpha_<k>.png, guide_<k>.png, trimap_<k>.png   [multi]
+```
+
+`<W>`는 이미지 폭이다. 장면마다 원본, 1/2, 1/4 세 가지가 있다 (예: 7680 / 3840 / 1920). `index.json`의 `tiers[].downsample`이 1, 2, 4다.
+
+## 예측을 만드는 법
+
+예측은 벤치마크와 같은 상대 경로에, 같은 크기의 8/16-bit grayscale PNG로 저장한다.
+
+```python
+import json, os, numpy as np
+from PIL import Image
+bench, pred = "/path/to/matting8k_v1", "/path/to/preds"
+index = json.load(open(f"{bench}/index.json"))
+for sc in index["scenes"]:
+    for t in sc["tiers"]:                      # 원하는 폭만 고르려면 t["width"]로 거른다
+        d = os.path.join(bench, t["path"]); f = t["files"]
+        img = np.asarray(Image.open(os.path.join(d, f["image"])))
+        out = os.path.join(pred, t["path"]); os.makedirs(out, exist_ok=True)
+        if sc["track"] == "single":
+            tri = np.asarray(Image.open(os.path.join(d, f["trimap_wide"])))
+            alpha = my_model(img, tri)          # float [0,1], (H, W)
+            Image.fromarray((alpha * 65535).astype(np.uint16)).save(f"{out}/alpha.png")
+        else:
+            os.makedirs(f"{out}/instances", exist_ok=True)
+            for k, v in f["instances"].items():
+                guide = np.asarray(Image.open(os.path.join(d, v["guide"])))
+                alpha = my_instance_model(img, guide)
+                Image.fromarray((alpha * 65535).astype(np.uint16)).save(f"{out}/instances/alpha_{k}.png")
+```
+
+## 평가
+
+```bash
+python evaluate.py --bench . --baseline gt                 # 정상이면 모든 오차가 0
+python evaluate.py --bench . --baseline trimap             # 비교 기준: unknown 영역을 0.5로 채움
+python evaluate.py --bench . --pred /path/to/preds --out results/            # 전체 (해상도 3가지 모두)
+python evaluate.py --bench . --pred /path/to/preds --scale 1 --out results/  # 원본 해상도만
+python evaluate.py --bench . --pred /path/to/preds --scale 4 --level 3       # 1/4 해상도, L3만
+# --no-conn: 8K에서 느린 connectivity 계산을 건너뛴다
+```
+
+지표는 alphamatting 규약을 따르고, α는 [0, 1] 범위다.
+
+| 지표 | 정의 |
+|---|---|
+| SAD | Σ\|p−g\|/1000 |
+| MAD | mean\|p−g\|. 해상도와 무관하므로 해상도 간 비교에는 이것을 쓴다 |
+| MSE | mean (p−g)² |
+| Grad | σ=1.4 Gaussian 기울기 차이의 제곱합 /1000 |
+| Grad_s | σ = 1.4·W/1920. 해상도가 달라도 같은 물리적 흐림 폭 |
+| Conn | connectivity 오차 /1000 |
+
+- **영역:** `unknown`(wide trimap = 128)과 `all`(이미지 전체) 두 가지로 보고한다.
+- **집계:** 트랙, 해상도 배율(1/1, 1/2, 1/4), 영역별로 내고, 카테고리와 난이도별로도 나눈다.
+- **해상도 간 비교:** 저해상도로 추론한 뒤 업샘플링하는 방법은, 같은 장면의 1/1과 1/4 결과를 MAD와 Grad_s로 비교하면 된다.
+- **출력:** `results/per_scene.csv`와 `results/summary.json`.
+
+## 알려진 한계
+
+- **굴절 유리** (샹들리에 유리, 안경 렌즈 등):
+  - 렌더러에서 굴절된 광선에는 배경판이 보이지 않는다. 그래서 α에는 에셋의 opacity map 값만 들어가고, 굴절된 배경은 F에 포함된다.
+  - 이 픽셀은 `ignore.png`로 표시되고 평가에서 빠진다.
+- **입력 형식:** 입력은 노출을 정규화한 뒤 sRGB로 변환했다. 밝은 하이라이트가 1을 넘는 곳은 8/16-bit에서 잘린다. α GT에는 영향이 없다.
+- **노이즈:** 256 spp라서 어두운 실내 장면에는 렌더 노이즈가 보인다. 이 노이즈는 입력과 GT에 똑같이 들어가 있으므로 GT 정확성에는 영향이 없다.
+- **에셋 수:** 전경 에셋은 Poly Haven 모델과 절차적 털·그물이다. v0+v1에서 Poly Haven 전경 에셋 40개를 썼고, 남은 미사용 에셋은 많지 않다.
+- **해상도:** 각 장면의 해상도는 에셋 텍스처가 받쳐주는 최대 해상도 이하로 정했다.

bench/matting8k_v1/evaluate.py

@@ -0,0 +1,194 @@
+"""Evaluate alpha predictions on a matting8k benchmark folder.
+
+  python evaluate.py --bench . --pred /path/to/preds [--scale 1|2|4] [--level 1-4] [--track single] --out results/
+  python evaluate.py --bench . --baseline trimap      # sanity check without a model
+  python evaluate.py --bench . --baseline gt          # must give all-zero errors
+
+Predictions mirror the benchmark layout:
+  <pred>/scenes/<sid>/<W>/alpha.png                    single track
+  <pred>/scenes/<sid>/<W>/instances/alpha_<k>.png      multi track (one per instance)
+8- or 16-bit grayscale PNG with the same size as image.png.
+
+Metrics (alphamatting.com / Rhemann et al. 2009 conventions, alpha in [0, 1]):
+  sad   sum |p - g| / 1000
+  mad   mean |p - g|                 (resolution independent; use it across tiers)
+  mse   mean (p - g)^2
+  grad  sum (|grad G_s * p| - |grad G_s * g|)^2 / 1000, s = 1.4
+  grad_s  same with s = 1.4 * W / 1920 (same physical blur at every tier)
+  conn  connectivity error / 1000 (threshold step 0.1)
+Regions: 'unknown' (wide trimap == 128) and 'all' (whole image); pixels in ignore.png
+(refractive glass, where alpha only holds the opacity map) are excluded from both.
+For the multi track each instance is scored against its own visible alpha.
+"""
+import argparse
+import csv
+import json
+import os
+import sys
+from collections import defaultdict
+
+import numpy as np
+from PIL import Image
+from scipy import ndimage
+
+Image.MAX_IMAGE_PIXELS = None
+
+
+def read_alpha(path):
+    a = np.asarray(Image.open(path))   # 16-bit PNGs load as uint16 or int32 ("I;16" / "I")
+    if a.ndim == 3:
+        a = a[..., 0]
+    return a.astype(np.float64) / (255.0 if a.dtype == np.uint8 else 65535.0)
+
+
+def gradient_mag(a, sigma):
+    gx = ndimage.gaussian_filter(a, sigma, order=(0, 1))
+    gy = ndimage.gaussian_filter(a, sigma, order=(1, 0))
+    return np.hypot(gx, gy)
+
+
+def connectivity(p, g, mask, step=0.1):
+    steps = np.arange(0, 1 + step, step)
+    l_map = -np.ones_like(g)
+    for i in range(1, len(steps)):
+        both = (p >= steps[i]) & (g >= steps[i])
+        lab, n = ndimage.label(both)
+        if n == 0:
+            omega = np.zeros_like(both)
+        else:
+            sizes = np.bincount(lab.ravel())
+            sizes[0] = 0
+            omega = lab == sizes.argmax()
+        flag = (l_map == -1) & ~omega
+        l_map[flag] = steps[i - 1]
+    l_map[l_map == -1] = 1
+    pd, gd = p - l_map, g - l_map
+    p_phi = 1 - pd * (pd >= 0.15)
+    g_phi = 1 - gd * (gd >= 0.15)
+    return float(np.abs(p_phi - g_phi)[mask].sum() / 1000)
+
+
+def metrics(p, g, regions, width, conn=True):
+    out = {}
+    gp, gg = gradient_mag(p, 1.4), gradient_mag(g, 1.4)
+    s = 1.4 * width / 1920
+    gps, ggs = (gp, gg) if abs(s - 1.4) < 1e-6 else (gradient_mag(p, s), gradient_mag(g, s))
+    d = np.abs(p - g)
+    for name, m in regions.items():
+        n = int(m.sum())
+        if n == 0:
+            continue
+        out[name] = {
+            "pixels": n,
+            "sad": float(d[m].sum() / 1000),
+            "mad": float(d[m].mean()),
+            "mse": float((d[m] ** 2).mean()),
+            "grad": float(((gp - gg)[m] ** 2).sum() / 1000),
+            "grad_s": float(((gps - ggs)[m] ** 2).sum() / 1000),
+            "conn": connectivity(p, g, m) if conn else float("nan"),
+        }
+    return out
+
+
+def baseline_pred(kind, gt, trimap):
+    if kind == "gt":
+        return gt
+    if kind == "trimap":   # known regions copied, unknown = 0.5
+        return np.where(trimap == 255, 1.0, np.where(trimap == 0, 0.0, 0.5))
+    raise ValueError(kind)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--bench", default=os.path.dirname(os.path.abspath(__file__)))
+    ap.add_argument("--pred", default=None)
+    ap.add_argument("--baseline", choices=["gt", "trimap"], default=None)
+    ap.add_argument("--tier", default="all", help="all | native | <width>")
+    ap.add_argument("--scale", type=int, default=None, choices=[1, 2, 4],
+                    help="only this downsample factor (1 = native, 2 = half, 4 = quarter)")
+    ap.add_argument("--level", type=int, default=None, help="only this difficulty level (1-4)")
+    ap.add_argument("--track", default="all", choices=["all", "single", "multi"])
+    ap.add_argument("--out", default=None)
+    ap.add_argument("--no-conn", action="store_true", help="skip the slow connectivity metric")
+    a = ap.parse_args()
+    if not (a.pred or a.baseline):
+        sys.exit("give --pred or --baseline")
+    index = json.load(open(os.path.join(a.bench, "index.json")))
+    rows, missing = [], []
+    for sc in index["scenes"]:
+        if a.track != "all" and sc["track"] != a.track:
+            continue
+        if a.level is not None and sc.get("level") != a.level:
+            continue
+        for t in sc["tiers"]:
+            if a.tier == "native" and t["downsample"] != 1:
+                continue
+            if a.scale and t["downsample"] != a.scale:
+                continue
+            if a.tier not in ("all", "native") and t["width"] != int(a.tier):
+                continue
+            d = os.path.join(a.bench, t["path"])
+            f = t["files"]
+            ignore = (np.asarray(Image.open(os.path.join(d, f["ignore"]))) > 0) if "ignore" in f else None
+            targets = []   # (instance id or None, gt alpha path, trimap path, pred rel path)
+            if sc["track"] == "single":
+                targets.append((None, f["alpha"], f["trimap_wide"], "alpha.png"))
+            else:
+                for k, v in f["instances"].items():
+                    targets.append((k, v["alpha"], v["trimap"], f"instances/alpha_{k}.png"))
+            for inst, ga, tri, pr in targets:
+                g = read_alpha(os.path.join(d, ga))
+                tm = np.asarray(Image.open(os.path.join(d, tri)))
+                if a.baseline:
+                    p = baseline_pred(a.baseline, g, tm)
+                else:
+                    pp = os.path.join(a.pred, t["path"], pr)
+                    if not os.path.exists(pp):
+                        missing.append(os.path.join(t["path"], pr))
+                        continue
+                    p = read_alpha(pp)
+                    if p.shape != g.shape:
+                        sys.exit(f"{pp}: shape {p.shape} != {g.shape}")
+                keep = np.ones_like(g, bool) if ignore is None else ~ignore
+                regions = {"unknown": (tm == 128) & keep, "all": keep}
+                m = metrics(np.clip(p, 0, 1), g, regions, t["width"], conn=not a.no_conn)
+                for region, vals in m.items():
+                    rows.append({"scene": sc["id"], "track": sc["track"], "category": sc["category"],
+                                 "level": sc.get("level"), "width": t["width"],
+                                 "scale": t["downsample"], "instance": inst,
+                                 "region": region, **vals})
+        print(f"[eval] {sc['id']}", file=sys.stderr, flush=True)
+    if missing:
+        print(f"[eval] {len(missing)} predictions missing, e.g. {missing[:3]}", file=sys.stderr)
+    # summary: mean over (scene, instance) per track / tier / region (+ per category, level)
+    keys = ["sad", "mad", "mse", "grad", "grad_s", "conn"]
+    groups = defaultdict(list)
+    for r in rows:
+        groups[(r["track"], r["scale"], r["region"], "ALL")].append(r)
+        groups[(r["track"], r["scale"], r["region"], f"category={r['category']}")].append(r)
+        if r["level"]:
+            groups[(r["track"], r["scale"], r["region"], f"level={r['level']}")].append(r)
+    summary = []
+    for (track, sc_, region, sub), rs in sorted(groups.items(), key=lambda x: x[0]):
+        summary.append({"track": track, "scale": sc_, "region": region, "subset": sub, "n": len(rs),
+                        "widths": sorted({r["width"] for r in rs}),
+                        **{k: float(np.mean([r[k] for r in rs])) for k in keys}})
+    if not rows:
+        sys.exit("no scenes evaluated")
+    hdr = f"{'track':6s} {'scale':>5s} {'region':8s} {'subset':24s} {'n':>4s} " + " ".join(f"{k:>9s}" for k in keys)
+    print(hdr)
+    for s in summary:
+        print(f"{s['track']:6s} {'1/' + str(s['scale']):>5s} {s['region']:8s} {s['subset'][:24]:24s} {s['n']:4d} "
+              + " ".join(f"{s[k]:9.4g}" for k in keys))
+    if a.out:
+        os.makedirs(a.out, exist_ok=True)
+        with open(os.path.join(a.out, "per_scene.csv"), "w", newline="") as fh:
+            wr = csv.DictWriter(fh, fieldnames=list(rows[0]))
+            wr.writeheader()
+            wr.writerows(rows)
+        json.dump({"summary": summary, "missing": missing}, open(os.path.join(a.out, "summary.json"), "w"),
+                  indent=1)
+
+
+if __name__ == "__main__":
+    main()

bench/matting8k_v1/index.json

@@ -0,0 +1,2850 @@
+{
+ "name": "matting8k_v1",
+ "version": 0,
+ "n_scenes": 24,
+ "skipped_not_pass": [],
+ "scenes": [
+  {
+   "id": "v1_8k__fur_101",
+   "source_run": "v1_8k/fur_101",
+   "track": "single",
+   "category": "fur",
+   "level": 1,
+   "native_resolution": [
+    7680,
+    4320
+   ],
+   "samples": 256,
+   "tiers": [
+    {
+     "width": 7680,
+     "height": 4320,
+     "downsample": 1,
+     "path": "scenes/v1_8k__fur_101/7680",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "alpha": "alpha.png",
+      "trimap_narrow": "trimap_narrow.png",
+      "trimap_wide": "trimap_wide.png"
+     },
+     "trimap_radius_px": {
+      "narrow": 56,
+      "wide": 113
+     }
+    },
+    {
+     "width": 3840,
+     "height": 2160,
+     "downsample": 2,
+     "path": "scenes/v1_8k__fur_101/3840",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "alpha": "alpha.png",
+      "trimap_narrow": "trimap_narrow.png",
+      "trimap_wide": "trimap_wide.png"
+     },
+     "trimap_radius_px": {
+      "narrow": 28,
+      "wide": 56
+     }
+    },
+    {
+     "width": 1920,
+     "height": 1080,
+     "downsample": 4,
+     "path": "scenes/v1_8k__fur_101/1920",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "alpha": "alpha.png",
+      "trimap_narrow": "trimap_narrow.png",
+      "trimap_wide": "trimap_wide.png"
+     },
+     "trimap_radius_px": {
+      "narrow": 14,
+      "wide": 28
+     }
+    }
+   ],
+   "fg": "torus",
+   "instances": [],
+   "unknown_fraction": 0.01982084297839506,
+   "instance_overlap_fraction": null,
+   "refractive_fraction_of_alpha": null,
+   "camera": {
+    "lens_mm": 35.0,
+    "fstop": 11.0,
+    "camera_distance_m": 0.831,
+    "elevation_deg": 13.9
+   },
+   "background": {
+    "bg_layout": "nature",
+    "bg_ground": "brick_floor",
+    "bg_wall": null,
+    "hdri": "ballawley_park"
+   },
+   "structure_width_px": 1.865
+  },
+  {
+   "id": "v1_8k__multi_L2_101",
+   "source_run": "v1_8k/multi_L2_101",
+   "track": "multi",
+   "category": "multi",
+   "level": 2,
+   "native_resolution": [
+    7680,
+    4320
+   ],
+   "samples": 256,
+   "tiers": [
+    {
+     "width": 7680,
+     "height": 4320,
+     "downsample": 1,
+     "path": "scenes/v1_8k__multi_L2_101/7680",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "instances": {
+       "0": {
+        "alpha": "instances/alpha_0.png",
+        "guide": "instances/guide_0.png",
+        "trimap": "instances/trimap_0.png"
+       },
+       "1": {
+        "alpha": "instances/alpha_1.png",
+        "guide": "instances/guide_1.png",
+        "trimap": "instances/trimap_1.png"
+       }
+      }
+     },
+     "trimap_radius_px": {
+      "narrow": 56,
+      "wide": 113
+     }
+    },
+    {
+     "width": 3840,
+     "height": 2160,
+     "downsample": 2,
+     "path": "scenes/v1_8k__multi_L2_101/3840",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "instances": {
+       "0": {
+        "alpha": "instances/alpha_0.png",
+        "guide": "instances/guide_0.png",
+        "trimap": "instances/trimap_0.png"
+       },
+       "1": {
+        "alpha": "instances/alpha_1.png",
+        "guide": "instances/guide_1.png",
+        "trimap": "instances/trimap_1.png"
+       }
+      }
+     },
+     "trimap_radius_px": {
+      "narrow": 28,
+      "wide": 56
+     }
+    },
+    {
+     "width": 1920,
+     "height": 1080,
+     "downsample": 4,
+     "path": "scenes/v1_8k__multi_L2_101/1920",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "instances": {
+       "0": {
+        "alpha": "instances/alpha_0.png",
+        "guide": "instances/guide_0.png",
+        "trimap": "instances/trimap_0.png"
+       },
+       "1": {
+        "alpha": "instances/alpha_1.png",
+        "guide": "instances/guide_1.png",
+        "trimap": "instances/trimap_1.png"
+       }
+      }
+     },
+     "trimap_radius_px": {
+      "narrow": 14,
+      "wide": 28
+     }
+    }
+   ],
+   "fg": null,
+   "instances": [
+    {
+     "id": 0,
+     "category": "plant",
+     "fg": "weed_plant_02"
+    },
+    {
+     "id": 1,
+     "category": "fur",
+     "fg": "ellipsoid"
+    }
+   ],
+   "unknown_fraction": 0.04751989293981482,
+   "instance_overlap_fraction": 0.0,
+   "refractive_fraction_of_alpha": null,
+   "camera": {
+    "lens_mm": 70.0,
+    "fstop": 2.8,
+    "camera_distance_m": 2.474,
+    "elevation_deg": 6.8
+   },
+   "background": {
+    "bg_layout": "nature",
+    "bg_ground": "brown_mud_leaves_01",
+    "bg_wall": null,
+    "hdri": "abandoned_pathway"
+   },
+   "structure_width_px": 0.766
+  },
+  {
+   "id": "v1_8k__multi_L2_102",
+   "source_run": "v1_8k/multi_L2_102",
+   "track": "multi",
+   "category": "multi",
+   "level": 2,
+   "native_resolution": [
+    3840,
+    2160
+   ],
+   "samples": 256,
+   "tiers": [
+    {
+     "width": 3840,
+     "height": 2160,
+     "downsample": 1,
+     "path": "scenes/v1_8k__multi_L2_102/3840",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "instances": {
+       "0": {
+        "alpha": "instances/alpha_0.png",
+        "guide": "instances/guide_0.png",
+        "trimap": "instances/trimap_0.png"
+       },
+       "1": {
+        "alpha": "instances/alpha_1.png",
+        "guide": "instances/guide_1.png",
+        "trimap": "instances/trimap_1.png"
+       },
+       "2": {
+        "alpha": "instances/alpha_2.png",
+        "guide": "instances/guide_2.png",
+        "trimap": "instances/trimap_2.png"
+       }
+      }
+     },
+     "trimap_radius_px": {
+      "narrow": 28,
+      "wide": 56
+     }
+    },
+    {
+     "width": 1920,
+     "height": 1080,
+     "downsample": 2,
+     "path": "scenes/v1_8k__multi_L2_102/1920",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "instances": {
+       "0": {
+        "alpha": "instances/alpha_0.png",
+        "guide": "instances/guide_0.png",
+        "trimap": "instances/trimap_0.png"
+       },
+       "1": {
+        "alpha": "instances/alpha_1.png",
+        "guide": "instances/guide_1.png",
+        "trimap": "instances/trimap_1.png"
+       },
+       "2": {
+        "alpha": "instances/alpha_2.png",
+        "guide": "instances/guide_2.png",
+        "trimap": "instances/trimap_2.png"
+       }
+      }
+     },
+     "trimap_radius_px": {
+      "narrow": 14,
+      "wide": 28
+     }
+    },
+    {
+     "width": 960,
+     "height": 540,
+     "downsample": 4,
+     "path": "scenes/v1_8k__multi_L2_102/960",
+     "files": {
+      "image": "image.png",
+      "image16": "image16.png",
+      "instances": {
+       "0": {
+        "alpha": "instances/alpha_0.png",
+        "guide": "instances/guide_0.png",
+        "trimap": "instances/trimap_0.png"
+       },
+       "1": {
+        "alpha": "instances/alpha_1.png",
+        "guide": "instances/guide_1.png",
+        "trimap": "instances/trimap_1.png"
+       },
+       "2": {
+        "alpha": "instances/alpha_2.png",
+        "guide": "instances/guide_2.png",
+        "trimap": "instances/trimap_2.png"
+       }
+      }
+     },
+     "trimap_radius_px": {
+      "narrow": 7,
+      "wide": 14
+     }
+    }
+   ],
+   "fg": null,
+   "instances": [
+    {
+     "id": 0,
+     "category": "object",
+     "fg": "gallinera_chair"
+    },
+    {
+     "id": 1,
+     "category": "object",
+     "fg": "pine_roots"
+    },
+    {
+     "id": 2,
+     "category": "fur",
+     "fg": "torus"
+    }
+   ],
+   "unknown_fraction": 0.004812765239197531,
+   "instance_overlap_fraction": 0.0,
+   "refractive_fraction_of_alpha": null,
+   "camera": {
+    "lens_mm": 50.0,
+    "fstop": 11.0,
+    "camera_distance_m": 7.422,
+    "elevation_deg": 7.7
+   },
+   "background": {
+    "bg_layout": "nature",
+    "bg_ground": "dry_ground_rocks",
+    "bg_wall": null,
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