What makes Cg Paper Writing notable?

1K downloads. Trust evidence available

How should Cg Paper Writing 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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Cg Paper Writing

Academic paper writing for 3D vision, computer graphics, CAD, and 3D understanding. Covers NeRF, 3DGS, SLAM, point cloud, 3D shape, CAD modeling. Supports CV... Skill: Cg Paper Writing Owner: jaccen Summary: Academic paper writing for 3D vision, computer graphics, CAD, and 3D understanding. Covers NeRF, 3DGS, SLAM, point cloud, 3D shape, CAD modeling. Supports CV... Tags: latest:1.2.2 Version history: v1.2.2 | 2026-05-19T07:38:09.889Z | auto Version 1.2.2 - Updated version number to 1.2.2 in SKILL.md. - No other content changes; documentation and guidance remain the same. v1

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.2.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 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: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.2.2release · observed May 19, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s172m233k07zcmx035knhdv0nh85v0ts:cg-paper-writing

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
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-cg-paper-writing/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

70,812 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: cg-paper-writing
description: "Academic paper writing for 3D vision, computer graphics, CAD, and 3D understanding. Covers NeRF, 3DGS, SLAM, point cloud, 3D shape, CAD modeling. Supports CVPR/ICCV/ECCV/SIGGRAPH venues."
version: 1.2.2
author: jaccen
tags: ["paper-writing", "academic", "computer-graphics", "3dgs", "nerf", "computer-vision", "cvpr", "siggraph"]
---

# 三维视觉与计算机图形学论文写作

面向三维重建、计算机图形学、CAD建模、3D理解与生成方向的学术写作辅助，覆盖从摘要到结论的全流程。

## 写作流程

### 摘要（Abstract）

结构：问题 → 不足 → 本文方法（一句话）→ 核心机制（1-2句）→ 实验结果（带数据）。

- 字数：CVPR/ICCV 150-250词；SIGGRAPH 200-300词；博士论文 500-800字
- 禁止：未定义缩写、引用、"we"以外的主语
- 必须包含：方法名称、核心指标数值、对比baseline

英文模板：
```
[Problem context, 1 sentence]
[Specific gap/limitation, 1-2 sentences]
[Our approach name and core idea, 1-2 sentences]
[Key technical mechanism, 1 sentence]
[Main results with numbers, 1-2 sentences]
[Broader impact or implication, 1 sentence]
```

### 引言（Introduction）

标准结构（适用于所有目标会议）：
1. 领域背景 + 该方向建立的基本范式（1段）
2. 已有工作的分类综述 + 各类方法的共性不足（1-2段）
3. 本文动机：从不足中引出研究问题（1段）
4. 本文方法概述：核心思想 + 2-3个关键设计（1段）
5. 实验总结：主要指标 + 对比优势（1段）

英文模板：
```
Paragraph 1: Problem context and importance
Paragraph 2: Existing approaches and their limitations
Paragraph 3: Our insight and high-level approach
Paragraph 4: Technical summary (what we actually do)
Paragraph 5: Contributions (bulleted, 3-4 items)
```

**引言写作禁忌**：
- 不在引言中展开数学公式（最多一个核心公式用于直观说明）
- 不在引言中列举实验细节（具体数字放实验部分）
- 避免通用乐观结尾（"我们相信本研究将推动该领域发展"）

### 相关工作（Related Work）

组织原则：按主题分组，而非按论文逐一罗列。

每个主题段落结构：
1. 该主题的共性方法（2-3句概括）
2. 代表性工作举例（带引用，说明每篇做了什么）
3. **关键**：与本文的区别（最后1-2句）

三维视觉论文常见分组：
- 神经辐射场与新视角合成（NeRF/3DGS及其变体）
- 点云处理与3D理解（分割/配准/检测）
- 3D生成与编辑（文本/图像到3D、形状编辑）
- CAD建模与逆向工程（参数化建模、特征提取）
- 3D场景理解与SLAM（语义重建、位姿估计）
- 高频/边界表达（如有符号方法、频域方法）
- 压缩与加速

英文模板：
```
Group by theme (not by paper):
- Section: "3D Gaussian Splatting and Variants"
- Section: "Neural Implicit Representations"
- Section: "[Your specific sub-area]"
Each section: Narrative flow with citations, not catalog.
End each section with: how existing work differs from yours.
```

### 方法（Methodology）

结构：总体框架图 → 各模块展开。

- 先给出整体pipeline/架构图（图1），后续逐模块引用
- 每个新符号首次出现时必须定义
- 公式编号连续，引用格式：式(1)、式(2)
- 每个模块结尾用1句话总结该模块的作用

英文模板：
```
3.1 Preliminary / Notation
3.2 [Core Component 1]
3.3 [Core Component 2]
3.4 Training / Optimization
3.5 [Implementation Details] (if space)
```

### 实验（Experiments）

必须包含的实验：
1. **数据集**：列出全部数据集，说明训练/测试划分
2. **评估指标**：根据方向选择（见下方各方向指标）
3. **基线对比**：至少包含当前SOTA
4. **消融实验**：逐一验证每个核心模块的贡献

**各方向核心评估指标**：

| 方向 | 核心指标 | 补充指标 |
|---|---|---|
| 新视角合成 | PSNR↑ SSIM↑ LPIPS↓ | FPS、基元数量 |
| 3D形状理解 | mIoU↑ mAcc↑ | F1-score、AUC |
| 3D生成 | FID↓、1-NNA-CD↓、1-NNA-EMD↓ | MMD、COV |
| 点云配准 | RMSE↓、Chamfer距离↓ | RRE、RTE |
| CAD重建 | Chamfer距离↓、F-score↑ | 几何精度 |
| 3D场景理解 | mIoU↑ | 查全率、查准率 |

可选加分项：
- 运行效率对比（FPS、训练时间、内存）
- 可视化对比（定性分析图）
- 不同场景难度（室内/室外、简单/复杂）
- 鲁棒性分析（噪声、遮挡、稀疏视角）

英文模板：
```
4.1 Experimental Setup (datasets, ba

_meta.json

{
  "ownerId": "kn7509ejxr3dh88hw796a4hh6x85vvtc",
  "slug": "cg-paper-writing",
  "version": "1.2.2",
  "publishedAt": 1779176289889
}

skill-card.md

## Description:

Academic paper writing for 3D vision, computer graphics, CAD, and 3D understanding. Covers NeRF, 3DGS, SLAM, point cloud, 3D shape, CAD modeling. Supports CVPR/ICCV/ECCV/SIGGRAPH venues.

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

## Publisher:

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

### License/Terms of Use:

MIT-0

## Use Case:

Researchers and developers use this skill to draft and revise academic papers in 3D vision, computer graphics, CAD, and 3D understanding, including common section structure, terminology, experiment reporting, and venue-specific writing expectations.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Users may treat venue rules, citation details, or recent-paper claims as authoritative when they may be outdated or incorrect.

Mitigation: Independently verify citations, venue requirements, and recent-paper claims before paper submission.

Risk: The skill may influence academic writing style without validating the truth of paper claims or reported results.

Mitigation: Use it as a writing-style helper and review all technical claims, measurements, and comparisons against the underlying research evidence.

## Reference(s):


## Skill Output:

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

**Output Format:** [Markdown prose and structured writing templates]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [No code execution; citation details, venue requirements, and recent-paper claims should be independently verified before submission.]

## Skill Version(s):

1.2.2 (source: frontmatter and server release metadata)

## 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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