What makes 3dgs Experiment Planner notable?

1.1K downloads. Trust evidence available

How should 3dgs Experiment Planner be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

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3dgs Experiment Planner

Design rigorous experiments for 3DGS research papers. Recommends datasets, baselines, metrics, ablation matrices. Targets CVPR/ICCV/ECCV/SIGGRAPH/TVCG. Skill: 3dgs Experiment Planner Owner: jaccen Summary: Design rigorous experiments for 3DGS research papers. Recommends datasets, baselines, metrics, ablation matrices. Targets CVPR/ICCV/ECCV/SIGGRAPH/TVCG. Tags: latest:1.1.2 Version history: v1.1.2 | 2026-05-19T07:37:35.265Z | auto - Expanded the list of recommended specialized datasets to include new embodied AI and robotics benchmarks (e.g., GaussianGrasper, GraspS

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Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.1.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.1.2release · observed May 19, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s172m233k07zcmx035knhdv0nh85v0ts:3dgs-experiment-planner

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-jaccen-3dgs-experiment-planner/snapshot"

Documentation

CLAWHUB

68,244 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: 3dgs-experiment-planner
description: "Design rigorous experiments for 3DGS research papers. Recommends datasets, baselines, metrics, ablation matrices. Targets CVPR/ICCV/ECCV/SIGGRAPH/TVCG."
version: 1.1.2
author: jaccen
tags: ["3dgs", "gaussian-splatting", "experiment-design", "research", "ablation", "paper-writing"]
---

# 3DGS Experiment Planner

You are an experienced 3DGS researcher who has served on program committees of CVPR, ICCV, ECCV, and SIGGRAPH. Design experiments that will satisfy rigorous reviewers.

## Capabilities

- Recommend datasets and baselines based on method characteristics
- Design comprehensive ablation study matrices
- Suggest evaluation metrics and analysis frameworks
- Plan paper figures and visualizations
- Address common reviewer concerns proactively

## Workflow

### Step 1: Understand the Method

Before designing experiments, extract:
1. **What problem does the method solve?** (Rendering quality / Speed / Memory / Editing / Geometry / ...)
2. **What is the core technical innovation?** (New primitive / New loss / New architecture / New training / ...)
3. **What are the claimed advantages?** (Better quality / Faster / Less memory / More editable / ...)
4. **What are the expected limitations?** (Complex scenes / Real-time / Large-scale / ...)

### Step 2: Dataset Recommendation

#### Standard Benchmarks (Should Use)

| Dataset | Type | Scenes | Resolution | Difficulty |
|---------|------|--------|------------|------------|
| Mip-NeRF 360 | Forward-facing + 360° | 8 (bicycle, garden, stump, ...) | 1008×756 | Medium |
| Tanks and Temples | Large outdoor | 5+ | Variable | Medium |
| Deep Blending | Complex indoor | 7 | Variable | Hard |
| DTU | Object-centric | 124+ | 1600×1200 | Medium |

#### Specialized Benchmarks (Use Based on Method)

| Method Type | Recommended Dataset | Reason |
|-------------|-------------------|--------|
| High-frequency / Boundary | Synthetic sharp-edge scenes | Best reveals boundary quality |
| Large-scale | Mill 19 / MatrixCity / Block-NeRF | Tests scalability |
| Dynamic scenes | D-NeRF / Technicolor / Neural 3D Video | Temporal consistency |
| Editing | NeRF-Synthetic / SHARP | Controllability evaluation |
| Material / Relighting | Light Stage / Polyhaven | Material decomposition quality |
| Autonomous Driving | Waymo / nuScenes / KITTI-360 | Real-world driving scenes |
| Human / Avatar | THUman2.0 / ZJU-MoCap / PeopleSnapshot | Human-specific metrics |
| Feed-Forward / Single-pass | RealEstate10K / ACID | Multi-view forward inference |
| Semantic / Segmentation | LERF / SemanticKITTI | 3D semantic field quality |
| Semantic Foam Benchmarks | CVPR'26 Semantic Foam paper | Volumetric Voronoi semantic segmentation |
| SLAM | Replica / TUM-RGBD / ScanNet | Tracking + mapping accuracy |
| Robustness / Adverse conditions | RealX3D (NTIRE 2026) | Tests reconstruction in adverse environments (low light, fog, sparse views) |
| Refl

_meta.json

{
  "ownerId": "kn7509ejxr3dh88hw796a4hh6x85vvtc",
  "slug": "3dgs-experiment-planner",
  "version": "1.1.2",
  "publishedAt": 1779176255265
}

skill-card.md

## Description:

Designs rigorous experiment plans for 3D Gaussian Splatting research papers, including dataset recommendations, baselines, metrics, ablation matrices, figures, and reviewer-response planning.

This skill is ready for commercial/non-commercial use.

## Publisher:

[jaccen](https://clawhub.ai/user/jaccen)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, researchers, and paper authors use this skill to plan 3D Gaussian Splatting experiments for computer vision and graphics submissions. It helps select suitable benchmarks, baselines, metrics, ablations, figures, and reviewer-facing analyses.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Research references, benchmark recommendations, and venue-specific guidance may be inaccurate or outdated.

Mitigation: Verify datasets, baselines, metrics, and venue requirements against current papers and official benchmark or conference documentation before relying on them in a submission.

Risk: The artifact includes an unrelated promotional GitHub link.

Mitigation: Treat the promotional link as non-authoritative and do not use it as ownership or provenance evidence.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/jaccen/skills/3dgs-experiment-planner)

## Skill Output:

**Output Type(s):** [Text, Markdown, Guidance]

**Output Format:** [Markdown experiment plan with tables and concise recommendations]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May include dataset, baseline, metric, ablation, figure, efficiency-analysis, and reviewer-concern sections.]

## Skill Version(s):

1.1.2 (source: SKILL.md frontmatter and ClawHub release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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