diff --git a/scripts/merge_multi_results.py b/scripts/merge_multi_results.py
index 80830aa..3a5626f 100644
--- a/scripts/merge_multi_results.py
+++ b/scripts/merge_multi_results.py
@@ -101,6 +101,12 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Skip HTML report",
     )
+    p.add_argument(
+        "--top-k",
+        type=int,
+        default=3,
+        help="Max classes shown per pixel click (rank = mean score + log area). Default 3.",
+    )
     return p.parse_args()
 
 
@@ -433,14 +439,42 @@ def mask_to_rle(mask: np.ndarray) -> list[int]:
     return rle
 
 
+def rank_score_mean_area(
+    scores_mean: float | None,
+    scores_max: float | None,
+    pixels: int,
+    image_pixels: int,
+) -> float:
+    """Priority for pixel label: mean confidence + log-normalized area.
+
+    rank = mean + log1p(pixels) / log1p(image_pixels)
+
+    - mean: SAM instance conf mean for that prompt (fallback: scores_max)
+    - area term in [0, 1]: larger masks rank higher, but log softens huge yards
+    Downstream 3D/point-cloud labeling can reuse the same formula for top-k.
+    """
+    if scores_mean is not None:
+        mean = float(scores_mean)
+    elif scores_max is not None:
+        mean = float(scores_max)
+    else:
+        mean = 0.0
+    img_px = max(int(image_pixels), 1)
+    px = max(int(pixels), 0)
+    area_term = float(np.log1p(px) / np.log1p(img_px))
+    return mean + area_term
+
+
 def export_hitmasks(
     out_dir: Path,
     ordered_items: list[dict[str, Any]],
     legend: list[dict[str, Any]],
     size: tuple[int, int],
+    top_k: int = 3,
 ) -> list[dict[str, Any]]:
     """Build interactive class meta with RLE hit data (file:// / no CORS needed)."""
     w, h = size
+    image_pixels = int(w * h)
     classes_meta: list[dict[str, Any]] = []
     for i, ent in enumerate(legend):
         it = None
@@ -466,9 +500,14 @@ def export_hitmasks(
         if smax is None:
             smax = it.get("scores_max")
         smean = it.get("scores_mean")
+        if smean is None:
+            smean = ent.get("scores_mean")
         if smean is None and smin is not None and smax is not None:
             smean = (float(smin) + float(smax)) / 2.0
 
+        pixels = int(ent.get("pixels") or int(mask.sum()))
+        rank = rank_score_mean_area(smean, smax, pixels, image_pixels)
+
         classes_meta.append(
             {
                 "id": i,
@@ -477,10 +516,11 @@ def export_hitmasks(
                 "n_objects": ent.get("n_objects")
                 if ent.get("n_objects") is not None
                 else it.get("n_objects"),
-                "pixels": int(ent.get("pixels") or int(mask.sum())),
+                "pixels": pixels,
                 "scores_min": smin,
                 "scores_max": smax,
                 "scores_mean": smean,
+                "rank_score": rank,
                 "rle": rle,
             }
         )
@@ -490,6 +530,13 @@ def export_hitmasks(
         "height": h,
         "overlay": "combined_overlay.png",
         "hit_mode": "rle",
+        "top_k": int(top_k),
+        "ranking": {
+            "formula": "mean + log1p(pixels)/log1p(W*H)",
+            "components": ["scores_mean", "log_area_norm"],
+            "purpose": "pixel prompt label for downstream point cloud",
+            "top_k": int(top_k),
+        },
         "classes": classes_meta,
     }
     meta_path = out_dir / "interactive_meta.json"
@@ -505,8 +552,9 @@ def write_html(
     legend: list[dict[str, Any]],
     meta: dict[str, Any],
     interactive_meta: dict[str, Any] | None = None,
+    top_k: int = 3,
 ) -> None:
-    """Write interactive index.html (click image → class list + scores)."""
+    """Write interactive index.html (click → top-k prompts by mean+area)."""
     # Embed meta so file:// works without fetch CORS issues
     imeta = interactive_meta
     if imeta is None:
@@ -521,7 +569,21 @@ def write_html(
                 "overlay": overlay_name,
                 "classes": [],
             }
+    # Ensure ranking policy is present for the embedded viewer / 3D export
+    imeta.setdefault("top_k", int(top_k))
+    imeta.setdefault(
+        "ranking",
+        {
+            "formula": "mean + log1p(pixels)/log1p(W*H)",
+            "components": ["scores_mean", "log_area_norm"],
+            "purpose": "pixel prompt label for downstream point cloud",
+            "top_k": int(top_k),
+        },
+    )
+    imeta["top_k"] = int(top_k)
+    imeta["ranking"]["top_k"] = int(top_k)
     imeta_js = json.dumps(imeta, ensure_ascii=False)
+    top_k = max(1, int(top_k))
 
     # Static fallback rows still useful if JS fails
     rows = []
@@ -650,27 +712,32 @@ header p {{ margin:.25rem 0 0; font-size:.85rem; color:rgba(255,255,255,.88); }}
 </head>
 <body>
 <header>
-  <h1>Merged multi-prompt — click to inspect</h1>
-  <p>이미지를 클릭하면 해당 픽셀에 겹친 클래스(프롬프트)와 confidence 점수가 오른쪽에 표시됩니다. 겹침 시 점수 높은 순으로 정렬됩니다.</p>
+  <h1>Merged multi-prompt — top-{top_k} pixel labels</h1>
+  <p>
+    픽셀 클릭 → 그 위치에 겹친 프롬프트 중
+    <strong>rank = mean conf + log 면적</strong> 상위 <strong>{top_k}</strong>개만 표시합니다.
+    (3D 포인트 클라우드 라벨용: 픽셀 → prompt)
+  </p>
 </header>
 <div class="layout">
   <div class="stage">
     <div class="canvas-wrap" id="wrap">
       <canvas id="view"></canvas>
     </div>
-    <p class="hint">클릭: 픽셀 조회 · Shift+클릭: 선택 유지(누적) · Esc: 초기화 · 클래스 {n_cls}개</p>
+    <p class="hint">클릭: top-{top_k} 조회 · Shift+클릭: 누적 후 재순위 top-{top_k} · Esc: 초기화 · 클래스 {n_cls}개</p>
     <p class="status" id="status">loading…</p>
     {grid_block}
   </div>
   <aside class="side">
     <div>
-      <h2>Selection</h2>
+      <h2>Top-{top_k} labels</h2>
       <div class="pick-meta" id="pickMeta">클릭하여 조회</div>
       <ul class="hit-list" id="hitList"></ul>
       <div class="empty-hit" id="emptyHit" hidden>이 픽셀에 마스크 없음</div>
       <p class="priority-note">
-        겹침 우선순위 참고: 목록은 <strong>scores_max</strong> 내림차순입니다.
-        점수가 비슷하면 픽셀 면적(n_objects)과 장면 문맥으로 판단하세요.
+        우선순위: <code>rank = scores_mean + log1p(pixels)/log1p(W×H)</code><br/>
+        mean 없을 때 scores_max 사용. 상위 <strong>{top_k}</strong>개만 표시 (나머지 중복 숨김).
+        이후 포인트클라우드에서 이 prompt를 픽셀 라벨로 씁니다.
       </p>
     </div>
     <div>
@@ -703,7 +770,9 @@ header p {{ margin:.25rem 0 0; font-size:.85rem; color:rgba(255,255,255,.88); }}
   let overlayImg = null;
   let classes = [];
   let lastHits = [];
+  let lastAllCount = 0;
   let marker = null;
+  const TOP_K = {top_k};
 
   function setStatus(msg, ok) {{
     statusEl.textContent = msg;
@@ -731,6 +800,32 @@ header p {{ margin:.25rem 0 0; font-size:.85rem; color:rgba(255,255,255,.88); }}
     return false;
   }}
 
+  /** rank = mean + log1p(pixels)/log1p(W*H) — same as Python rank_score_mean_area */
+  function computeRankScore(cls) {{
+    if (cls.rank_score != null && Number.isFinite(Number(cls.rank_score))) {{
+      return Number(cls.rank_score);
+    }}
+    let mean = 0;
+    if (cls.scores_mean != null) mean = Number(cls.scores_mean);
+    else if (cls.scores_max != null) mean = Number(cls.scores_max);
+    const imgPx = Math.max(1, (meta.width || 1) * (meta.height || 1));
+    const px = Math.max(0, Number(cls.pixels) || 0);
+    const areaTerm = Math.log1p(px) / Math.log1p(imgPx);
+    return mean + areaTerm;
+  }}
+
+  function sortByRank(hits) {{
+    return hits.slice().sort((a, b) => {{
+      const d = b.rank_score - a.rank_score;
+      if (d !== 0) return d;
+      // tie-break: higher mean, then larger area
+      const ma = a.scores_mean != null ? Number(a.scores_mean) : -1;
+      const mb = b.scores_mean != null ? Number(b.scores_mean) : -1;
+      if (mb !== ma) return mb - ma;
+      return (b.pixels || 0) - (a.pixels || 0);
+    }});
+  }}
+
   function draw() {{
     if (!overlayImg || !meta) return;
     const w = meta.width, h = meta.height;
@@ -797,32 +892,38 @@ header p {{ margin:.25rem 0 0; font-size:.85rem; color:rgba(255,255,255,.88); }}
           scores_mean: cls.scores_mean,
           n_objects: cls.n_objects,
           pixels: cls.pixels,
-          rank_score: (cls.scores_max != null ? Number(cls.scores_max) : -1),
+          rank_score: computeRankScore(cls),
         }});
       }}
     }}
-    hits.sort((a, b) => b.rank_score - a.rank_score || (b.pixels || 0) - (a.pixels || 0));
+    let ranked = sortByRank(hits);
+    lastAllCount = ranked.length;
 
     if (accumulate && lastHits.length) {{
       const map = new Map(lastHits.map(h => [h.id, h]));
-      for (const h of hits) map.set(h.id, h);
-      lastHits = Array.from(map.values()).sort(
-        (a, b) => b.rank_score - a.rank_score || (b.pixels || 0) - (a.pixels || 0)
-      );
-    }} else {{
-      lastHits = hits;
+      for (const h of ranked) map.set(h.id, h);
+      ranked = sortByRank(Array.from(map.values()));
+      lastAllCount = ranked.length;
     }}
+    // Only top-K labels for UI and for downstream "pixel → prompt" selection
+    lastHits = ranked.slice(0, TOP_K);
     marker = {{ x, y }};
     renderHits();
     draw();
   }}
 
   function fmtScore(h) {{
-    if (h.scores_max == null && h.scores_min == null) return 'score n/a';
-    const a = h.scores_min != null ? Number(h.scores_min).toFixed(3) : '?';
-    const b = h.scores_max != null ? Number(h.scores_max).toFixed(3) : '?';
+    if (h.scores_max == null && h.scores_min == null && h.scores_mean == null) {{
+      return 'score n/a';
+    }}
     const m = h.scores_mean != null ? Number(h.scores_mean).toFixed(3) : null;
-    return m != null ? `max ${{b}} · mean ${{m}}` : `${{a}} ~ ${{b}}`;
+    const b = h.scores_max != null ? Number(h.scores_max).toFixed(3) : null;
+    const r = h.rank_score != null ? Number(h.rank_score).toFixed(3) : null;
+    const parts = [];
+    if (m != null) parts.push('mean ' + m);
+    if (b != null) parts.push('max ' + b);
+    if (r != null) parts.push('rank ' + r);
+    return parts.join(' · ') || 'score n/a';
   }}
 
   function renderHits() {{
@@ -837,18 +938,21 @@ header p {{ margin:.25rem 0 0; font-size:.85rem; color:rgba(255,255,255,.88); }}
       return;
     }}
     emptyHit.hidden = true;
-    pickMeta.textContent = `pixel (${{marker.x}}, ${{marker.y}}) — ${{lastHits.length}} class(es) overlapping`;
+    const hidden = Math.max(0, lastAllCount - lastHits.length);
+    pickMeta.textContent = hidden > 0
+      ? `pixel (${{marker.x}}, ${{marker.y}}) — top ${{lastHits.length}} / ${{lastAllCount}} overlapping (hidden ${{hidden}})`
+      : `pixel (${{marker.x}}, ${{marker.y}}) — top ${{lastHits.length}} / ${{lastAllCount}} overlapping`;
 
     lastHits.forEach((h, i) => {{
       const li = document.createElement('li');
       if (i === 0) li.classList.add('top');
       li.style.borderLeftColor = `rgb(${{h.color[0]}},${{h.color[1]}},${{h.color[2]}})`;
-      const rank = i === 0 ? '<span class="rank-badge">priority #1</span>' : `<span class="rank-badge">#${{i+1}}</span>`;
+      const rank = `<span class="rank-badge">#${{i+1}}</span>`;
       li.innerHTML = `
         <span class="sw" style="background:rgb(${{h.color[0]}},${{h.color[1]}},${{h.color[2]}})"></span>
         <div><div class="name">${{h.prompt}}${{rank}}</div></div>
         <div class="score">${{fmtScore(h)}}</div>
-        <div class="sub">n_objects=${{h.n_objects ?? '-'}} · pixels=${{(h.pixels != null ? Number(h.pixels).toLocaleString() : '-')}}</div>
+        <div class="sub">n_objects=${{h.n_objects ?? '-'}} · pixels=${{(h.pixels != null ? Number(h.pixels).toLocaleString() : '-')}} · rank=${{Number(h.rank_score).toFixed(3)}}</div>
       `;
       hitList.appendChild(li);
 
@@ -875,11 +979,16 @@ header p {{ margin:.25rem 0 0; font-size:.85rem; color:rgba(255,255,255,.88); }}
       scores_mean: c.scores_mean,
       n_objects: c.n_objects,
       pixels: c.pixels,
+      rank_score: c.rank_score,
     }}));
     setStatus('loading overlay…');
     overlayImg = await loadImage(meta.overlay || '{overlay_name}');
     draw();
-    setStatus('ready — ' + classes.length + ' classes · click image (RLE hit-test)', true);
+    setStatus(
+      'ready — ' + classes.length + ' classes · click → top-' + TOP_K +
+      ' by mean+log(area) (RLE)',
+      true
+    );
   }} catch (e) {{
     setStatus(String(e.message || e), false);
     try {{
@@ -1042,8 +1151,11 @@ def main() -> int:
         print(f"saved: {grid_path}")
 
     # RLE hit data for interactive click query (file:// safe, no getImageData)
-    print("exporting RLE hit data for interactive HTML...")
-    classes_meta = export_hitmasks(out_dir, ordered, legend, base.size)
+    top_k = max(1, int(args.top_k))
+    print(f"exporting RLE hit data for interactive HTML (top_k={top_k})...")
+    classes_meta = export_hitmasks(
+        out_dir, ordered, legend, base.size, top_k=top_k
+    )
     print(f"saved: {out_dir / 'interactive_meta.json'} ({len(classes_meta)} classes, RLE)")
 
     merge_summary = {
@@ -1052,6 +1164,13 @@ def main() -> int:
         "n_classes": len(legend),
         "max_side": args.max_side,
         "alpha": args.alpha,
+        "top_k": top_k,
+        "ranking": {
+            "formula": "mean + log1p(pixels)/log1p(W*H)",
+            "components": ["scores_mean", "log_area_norm"],
+            "purpose": "pixel prompt label for downstream point cloud",
+            "top_k": top_k,
+        },
         "overview_size": list(base.size),
         "classes": legend,
         "outputs": {
@@ -1075,6 +1194,8 @@ def main() -> int:
             "width": base.size[0],
             "height": base.size[1],
             "overlay": overlay_path.name,
+            "top_k": top_k,
+            "ranking": merge_summary["ranking"],
             "classes": classes_meta,
         }
         # Use overlay without side legend so click coords map 1:1 to hitmasks
@@ -1085,8 +1206,9 @@ def main() -> int:
             legend=legend,
             meta=merge_summary,
             interactive_meta=interactive_meta,
+            top_k=top_k,
         )
-        print(f"saved: {html_path} (interactive click inspect)")
+        print(f"saved: {html_path} (interactive top-{top_k} by mean+log area)")
 
     print("\nClasses merged:")
     for ent in legend: