/3dgs-visualizer

name: 3dgs-visualizer
description: 'Generate publication-quality visualizations for 3DGS research: radar charts, comparison tables, method
  timelines. Static (PDF/PNG) and interactive (HTML) output. Use when: creating comparison charts for 3DGS papers,
  visualizing method capabilities, generating method timelines or radar plots, 3DGS可视化/论文配图/方法对比图表.'
license: Apache-2.0
metadata:
  version: 1.5.0
  author: jaccen
  tags:
  - 3dgs
  - gaussian-splatting
  - visualization
  - radar-chart
  - timeline
  - research
  when_to_use:
  - Create comparison charts for 3DGS papers
  - Visualize method capabilities with radar plots
  - Generate method timelines or chronological evolution charts
  - Produce publication-quality figures (PDF/PNG/HTML)
  - Build interactive comparison tables for 3DGS methods
  - 3DGS可视化 / 论文配图 / 方法对比图表 / 雷达图 / 时间线
  platform-fields:
    user-invocable: true

3DGS Visualizer — Publication-Quality Research Visualizations

Generate publication-quality charts for 3DGS method landscape comparison and evolution tracking.

Capabilities

• **Radar Charts**: Multi-dimensional method capability comparison
• **Comparison Tables**: Visual performance/efficiency tables with highlighting
• **Method Timelines**: Chronological evolution showing trends and paradigm shifts
• **Dual Output**: Static (PDF/PNG via matplotlib) and interactive HTML (via plotly)

Data Sources

| File | Content | |------|---------| | `../../references/3dgs-methods-overview.md` | Master index, metrics summary | | `../../references/methods-core.md` | Foundation, Geometry, CAD, Generation, Feed-Forward, Compression, Dynamic | | `../../references/methods-semantic-editing.md` | Semantic, Editing, Avatar, Material methods | | `../../references/methods-systems-apps.md` | Robustness, Driving, SLAM, Simulation, Cross-Domain | | `../../references/baselines.md` | Standard baselines with core metrics | | `../../references/experiments.md` | Dataset configs, efficiency reference values |

---

Visualization 1: Radar Charts (Method Capability Comparison)

**When to use**: Comparing 3–8 methods across multiple dimensions; showing quality/speed/memory trade-offs; use-case recommendation.

Dimensions

| Dimension | Scoring Criteria (0–10) | |-----------|------------------------| | **Render Quality** | 10=SOTA, 7=competitive, 5=acceptable, 3=below baseline | | **Render Speed** | 10=200+ FPS, 7=60–100, 5=30–60, 3=<30 | | **Memory Efficiency** | 10=<50MB, 7=100–500MB, 5=0.5–2GB, 3=>2GB | | **Geometry Quality** | 10=mesh-ready (2DGS/SuGaR), 7=decent depth, 5=approx, 3=poor | | **Scalability** | 10=city-scale, 7=building, 5=room, 3=object-only | | **Ease of Use** | 10=single script, 7=standard pipeline, 5=multi-stage, 3=complex setup | | **Novelty** | 10=paradigm shift, 7=significant extension, 5=incremental, 3=minor tweak |

Adjust dimensions by context (compression: add "Compression Ratio"; avatar: add "Expression Fidelity"; SLAM: add "Tracking Accuracy").

API

OKABE_ITO = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
             '#0072B2', '#D55E00', '#CC79A7', '#000000']

# Static (matplotlib)
def plot_radar(methods_data, dimensions, title="3DGS Method Comparison",
               output_path="radar_comparison.pdf", figsize=(8, 8)):
    """methods_data: {name: [score1, ...]}, dimensions: [label, ...]"""
    N = len(dimensions)
    angles = np.linspace(0, 2*np.pi, N, endpoint=False).tolist()
    angles += angles[:1]
    fig, ax = plt.subplots(figsize=figsize, subplot_kw=dict(polar=True))
    for i, (name, values) in enumerate(methods_data.items()):
        values = values + values[:1]
        ax.plot(angles, values, 'o-', linewidth=2, label=name, color=OKABE_ITO[i%8])
        ax.fill(angles, values, alpha=0.1, color=OKABE_ITO[i%8])
    ax.set_xticks(angles[:-1]); ax.set_xticklabels(dimensions, fontsize=10)
    ax.set_ylim(0, 10); ax.set_yticks([2,4,6,8,10])
    ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1), fontsize=9)
    ax.grid(color='grey', linewidth=0.3, alpha=0.5)
    plt.tight_layout()
    plt.savefig(output_path, dpi=300, bbox_inches='tight', facecolor='white')
    plt.savefig(output_path.replace('.pdf','.png'), dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()

# Interactive (plotly)
def plot_radar_interactive(methods_data, dimensions, title="3DGS Method Comparison",
                           output_path="radar_comparison.html"):
    fig = go.Figure()
    for i, (name, values) in enumerate(methods_data.items()):
        fig.add_trace(go.Scatterpolar(
            r=values+values[:1], theta=dimensions+dimensions[:1],
            fill='toself', name=name, line_color=OKABE_ITO[i%8], opacity=0.8))
    fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[0,10])),
        showlegend=True, title=dict(text=title), width=900, height=700)
    fig.write_html(output_path)

---

Visualization 2: Comparison Tables (Visual Performance Tables)

**When to use**: Summarizing quantitative results across methods/datasets; paper-ready tables with visual emphasis; efficiency vs quality trade-off.

Table Types

| Type | Description | Best For | |------|-------------|----------| | **A: Quantitative Performance** | Color-coded cells (green=best, blue=second) | Multi-dataset metric comparison | | **B: Efficiency-Quality Scatter** | FPS vs PSNR scatter with category coloring | Speed/quality trade-off analysis |

API — Type A: Performance Table

def plot_comparison_table(data, methods, datasets, metric="PSNR (dB)",
                          higher_is_better=True, output_path="perf_table.pdf"):
    """data: 2D array [method][dataset]"""
    fig, ax = plt.subplots(figsize=(len(datasets)*1.8+2, len(methods)*0.6+1))
    ax.axis('off')
    cell_text, cell_colors = [], []
    for i in range(len(datasets)):
        row, row_colors = [], []
        col_vals = [data[k][i] for k in range(len(methods))]
        for j in range(len(methods)):
            val = da