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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

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

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

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 = data[j][i]; row.append(f"{val:.2f}")
            is_best = abs(val - (max if higher_is_better else min)(col_vals)) < 0.01
            is_second = abs(val - sorted(col_vals, reverse=higher_is_better)[1]) < 0.01 if len(col_vals)>1 else False
            row_colors.append('#C6EFCE' if is_best else '#BDD7EE' if is_second else '#FFFFFF')
        cell_text.append(row); cell_colors.append(row_colors)
    table = ax.table(cellText=cell_text, rowLabels=datasets, colLabels=methods,
                     cellColours=cell_colors, loc='center', cellLoc='center')
    table.auto_set_font_size(False); table.set_fontsize(10); table.scale(1, 1.8)
    for j in range(len(methods)):
        table[0,j].set_facecolor('#4472C4'); table[0,j].set_text_props(color='white', fontweight='bold')
    ax.set_title(f"{metric} Comparison", fontsize=14, fontweight='bold', pad=20)
    plt.tight_layout(); plt.savefig(output_path, dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()

API — Type B: Efficiency Scatter

CATEGORY_COLORS = {
    'Foundation': '#0072B2', 'Compression': '#E69F00', 'Feed-Forward': '#009E73',
    'Geometry': '#D55E00', 'Dynamic': '#CC79A7', 'Other': '#56B4E9',
    'Surface/Geometry': '#D55E00', 'Editing': '#56B4E9', 'Semantic/Language': '#F0E442',
    'Avatar/Human': '#994F00', 'SLAM': '#661100', 'Cross-Domain': '#5B5B5B',
    'Robustness': '#984EA3', 'Generation': '#4daf4a', 'System/Acceleration': '#377eb8', 'CAD/Mesh': '#ff7f00',
}

def plot_efficiency_scatter(methods_info, output_path="efficiency_scatter.pdf"):
    """methods_info: [{name, psnr, fps, category, size}]"""
    fig, ax = plt.subplots(figsize=(8, 6))
    for info in methods_info:
        color = CATEGORY_COLORS.get(info.get('category','Other'), '#56B4E9')
        ax.scatter(info['fps'], info['psnr'], s=info.get('size',100),
                   c=color, alpha=0.8, edgecolors='black', linewidth=0.5)
        ax.annotate(info['name'], (info['fps'], info['psnr']),
                    textcoords="offset points", xytext=(5,5), fontsize=8)
    ax.set_xlabel('Rendering Speed (FPS)'); ax.set_ylabel('PSNR (dB)')
    ax.axhline(y=27, color='grey', linestyle='--', alpha=0.3)
    ax.axvline(x=60, color='grey', linestyle='--', alpha=0.3)
    ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False)
    plt.tight_layout(); plt.savefig(output_path, dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()

# Interactive table (plotly)
def plot_interactive_table(data, methods, datasets, metric="PSNR (dB)",
                           output_path="perf_table.html"):
    fig = go.Figure(data=[go.Table(
        header=dict(values=[metric]+methods, fill_color='#4472C4', font=dict(color='white', size=12)),
        cells=dict(values=[[f"{v:.2f}" for v in col] for col in zip(*data)], fill_color='white'))])
    fig.update_layout(width=800, title=metric); fig.write_html(output_path)

Visualization 3: Method Timelines (3DGS Evolution)

When to use: Chronological development; identifying research trends; literature review figures; conference slides.

Design Principles

• Horizontal axis: Time (year/quarter)
• Vertical lanes: Research categories
• Node size: Significance (citation count)
• Node color: Category (use CATEGORY_COLORS, consistent with other charts)
• Connections: Show lineage (e.g., 3DGS → Scaffold-GS, 3DGS → 2DGS)
• Award markers: Add ★ for best paper (D4RT, CVPR 2026) and ☆ for best student paper (TRELLIS.2, CVPR 2026) when annotating timeline nodes

CVPR 2026 Key Methods for Timeline Annotation

When generating timelines that include 2026 methods, highlight these as landmark entries:

Knowledge base: 690+ methods across 25 categories (updated for v0.3.3 cycle).

API — Static Timeline

def plot_timeline(events, output_path="3dgs_timeline.pdf", figsize=(16, 10)):
    """events: [{name, date(YYYY-MM), category, venue, citation_count}]"""
    fig, ax = plt.subplots(figsize=figsize)
    y_positions = {cat: i for i, cat in enumerate(sorted(set(e['category'] for e in events)))}
    for event in events:
        y = y_positions[event['category']]
        dt = datetime.strptime(event['date'][:7], '%Y-%m')
        x = mdates.date2num(dt)
        color = CATEGORY_COLORS.get(event['category'], '#666666')
        size = min(200, 50 + event.get('citation_count', 20) * 0.5)
        ax.scatter(x, y, s=size, c=color, alpha=0.8, edgecolors='black', linewidth=0.5, zorder=5)
        venue = event.get('venue', '')
        label = f"{event['name']}\n({venue})" if venue else event['name']
        ax.annotate(label, (x, y), textcoords="offset points",
                    xytext=(0, -size**0.5/2 - 8), ha='center', fontsize=6,
                    bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.8,
                              edgecolor=color, linewidth=0.5))
    ax.set_yticks(range(len(y_positions)))
    ax.set_yticklabels(sorted(y_positions.keys()), fontsize=10)
    ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
    plt.xticks(rotation=45, fontsize=9)
    ax.set_title('3DGS Method Evolution Timeline', fontsize=16, fontweight='bold')
    ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False)
    plt.tight_layout(); plt.savefig(output_path, dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()

API — Interactive Timeline

def plot_timeline_interactive(events, output_path="3dgs_timeline.html"):
    categories = sorted(set(e['category'] for e in events))
    y_map = {cat: i for i, cat in enumerate(categories)}
    fig = go.Figure()
    for cat in categories:
        cat_events = [e for e in events if e['category'] == cat]
        dates = [datetime.strptime(e['date'][:7], '%Y-%m') for e in cat_events]
        y_vals = [y_map[cat]] * len(cat_events)
        sizes = [min(30, 10+e.get('citation_count',20)*0.1) for e in cat_events]
        hover = [f"<b>{e['name']}</b><br>Venue: {e.get('venue','N/A')}<br>"
                 f"Citations: {e.get('citation_count','N/A')}" for e in cat_events]
        fig.add_trace(go.Scatter(x=dates, y=y_vals, mode='markers+text', name=cat,
            marker=dict(size=sizes, color=CATEGORY_COLORS.get(cat,'#666')),
            text=[e['name'] for e in cat_events], textposition='bottom center',
            textfont=dict(size=8), hovertext=hover, hoverinfo='text'))
    fig.update_layout(title='3DGS Method Evolution Timeline', height=800, width=1200,
        yaxis=dict(tickmode='array', tickvals=list(range(len(categories))), ticktext=categories),
        hovermode='closest', legend=dict(orientation="h", y=-0.15))
    fig.write_html(output_path)

Workflow

1. Identify: Visualization type (Radar/Table/Timeline), methods, output format (static/interactive/both), context (paper/presentation/comparison)
2. Gather Data: Read references/*.md for metrics; score qualitative dimensions from knowledge base; prefer user-provided data when given
3. Generate: Write Python script to .temp/; apply publication-quality styling; export PDF/PNG + HTML
4. Validate: Check readability, colorblind accessibility (Okabe-Ito), label positioning

Pre-built Presets

• Landscape Overview: Radar + scatter + timeline combined (3 PDFs + interactive HTML)
• Category Deep Dive: Category-specific radar dimensions + detailed table + mini-timeline
• Paper Submission Package: Comparison radar (Related Work) + performance table + efficiency scatter, all at 300 DPI

Integration

• scientific-visualization: Publication styling, journal formatting, DPI
• 3dgs-method-compare: Comparison results as data source
• 3dgs-experiment-planner: Ablation figure generation
• 3dgs-paper-reader: Extract metrics from new papers

Rules

1. Data accuracy first: Prefer knowledge base data over estimates; mark uncertain values as "approx."
2. Color consistency: Same category-to-color mapping across all charts in one output
3. Accessibility: Okabe-Ito palette default; test grayscale readability
4. No chart junk: Remove unnecessary gridlines, 3D effects, shadows
5. Proper labeling: All axes with units; clear legends
6. Citation awareness: Include venue/year for method context
7. Interactive bonus: Always offer interactive HTML alongside static figures

Red Lines

The following are categorical prohibitions. Violating any of these invalidates the output:

• No invented data: Never fabricate visualization comparison data, rendering performance numbers, or method capability claims. If a value is not found in the loaded files, write "data not available" or "N/A".
• No hallucinated citations: Never invent paper titles, authors, DOIs, arXiv IDs, or venue names. Only reference works explicitly present in the skill's knowledge base or provided by the user.
• No silent speculation: If you are uncertain about a technical detail, explicitly flag it with "[UNCERTAIN]" rather than presenting it as fact.
• No method misattribution: Do not assign features, results, or mechanisms from one method to another. Each method's data is specific to that method.
• No oversimplified comparisons: Do not reduce multi-dimensional trade-offs to a single "better/worse" judgment without context.

Related Skills

• 3dgs-method-compare — Method comparison (use comparison data to generate radar charts)
• 3dgs-experiment-planner — Experiment design (use experiment results for comparison plots)
• cg-paper-writing — Paper writing (use visualizations in manuscript figures)

Guardrail: Do Not Apply From Memory

Do NOT try to apply the logic, method data, bug patterns, or technical details described in this skill from memory. Always read the SKILL.md and referenced files from disk before producing any output. The knowledge base is updated frequently; stale memory may produce outdated, inaccurate, or fabricated results.

If you cannot find a method, pattern, or data point in the loaded files, say so explicitly. Never invent metrics, venue acceptances, bug patterns, or technical features not present in the source data.

Source: jaccen/Awesome-Gaussian-Skills — distributed by TomeVault.