什么是 计算机视觉专家？

计算机视觉专家是 Openclaw 技能库中的一项先进技术资源，旨在指导开发者实现下一代视觉智能。它专注于高性能架构，包括用于无 NMS 实时检测的 YOLO26 和用于高级文本到掩码分割的 Segment Anything 3 (SAM 3)。

该技能弥合了经典几何与现代深度学习之间的差距，提供 Depth Anything V2 空间感知和精密工程子像素校准方面的专业知识。通过利用此技能，开发者可以构建强大的流水线，处理从单目深度估计到使用前沿视觉语言模型进行视觉问答的所有任务。

下载入口:https://github.com/openclaw/skills/tree/main/skills/zorrong/computer-vision-expert

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

计算机视觉专家 应用场景

• 使用无 NMS 的 YOLO26 架构构建低延迟实时对象检测系统。
• 通过 SAM 3 文本引导分割实现工业检查任务自动化。
• 使用视觉 SLAM 和单目深度估计开发自主导航系统。
• 优化计算机视觉模型，以便部署在 NPU 和 TensorRT 等边缘硬件上。
• 使用现代 VLM 实现语义场景理解和视觉接地。

1. 确定视觉目标，如对象检测、像素级分割或 3D 场景重建。
2. 选择合适的 SOTA 基础模型，利用 YOLO26 实现速度或利用 SAM 3 实现零样本可提示任务。
3. 配置视觉流水线，整合基于文本的接地或视觉推理模块，进行复杂的语义分析。
4. 应用几何校准和深度估计，将 2D 输入转换为具有空间感知能力的 3D 表示。
5. 使用 MuSGD 优化器和简化的模块结构导出并优化针对目标硬件的流水线，实现高效推理。

计算机视觉专家 配置指南

要将此功能集成到您的项目中，请确保已安装并初始化 Openclaw Skills CLI。按照以下步骤配置环境：

# 安装计算机视觉专家技能
openclaw install computer-vision-expert

# 初始化支持 YOLO26 和 SAM 3 的视觉工作区
openclaw vision-init --sota-2026

# 验证 ONNX/TensorRT 的边缘部署兼容性
openclaw vision-check --target edge-npu

计算机视觉专家 数据架构与分类体系

该技能通过标准化模式管理复杂的视觉数据和元数据，以确保不同视觉模块之间的互操作性。

name: computer-vision-expert
description: SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

Computer Vision Expert (SOTA 2026)

Role: Advanced Vision Systems Architect & Spatial Intelligence Expert

Purpose

To provide expert guidance on designing, implementing, and optimizing state-of-the-art computer vision pipelines. From real-time object detection with YOLO26 to foundation model-based segmentation with SAM 3 and visual reasoning with VLMs.

When to Use

• Designing high-performance real-time detection systems (YOLO26).
• Implementing zero-shot or text-guided segmentation tasks (SAM 3).
• Building spatial awareness, depth estimation, or 3D reconstruction systems.
• Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU).
• Needing to bridge classical geometry (calibration) with modern deep learning.

Capabilities

1. Unified Real-Time Detection (YOLO26)

• NMS-Free Architecture: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity).
• Edge Deployment: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer.
• Improved Small-Object Recognition: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings.

2. Promptable Segmentation (SAM 3)

• Text-to-Mask: Ability to segment objects using natural language descriptions (e.g., "the blue container on the right").
• SAM 3D: Reconstructing objects, scenes, and human bodies in 3D from single/multi-view images.
• Unified Logic: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2.

3. Vision Language Models (VLMs)

• Visual Grounding: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding.
• Visual Question Answering (VQA): Extracting structured data from visual inputs through conversational reasoning.

4. Geometry & Reconstruction

• Depth Anything V2: State-of-the-art monocular depth estimation for spatial awareness.
• Sub-pixel Calibration: Chessboard/Charuco pipelines for high-precision stereo/multi-camera rigs.
• Visual SLAM: Real-time localization and mapping for autonomous systems.

Patterns

1. Text-Guided Vision Pipelines

• Use SAM 3's text-to-mask capability to isolate specific parts during inspection without needing custom detectors for every variation.
• Combine YOLO26 for fast "candidate proposal" and SAM 3 for "precise mask refinement".

2. Deployment-First Design

• Leverage YOLO26's simplified ONNX/TensorRT exports (NMS-free).
• Use MuSGD for significantly faster training convergence on custom datasets.

3. Progressive 3D Scene Reconstruction

• Integrate monocular depth maps with geometric homographies to build accurate 2.5D/3D representations of scenes.

Anti-Patterns

• Manual NMS Post-processing: Stick to NMS-free architectures (YOLO26/v10+) for lower overhead.
• Click-Only Segmentation: Forgetting that SAM 3 eliminates the need for manual point prompts in many scenarios via text grounding.
• Legacy DFL Exports: Using outdated export pipelines that don't take advantage of YOLO26's simplified module structure.

Sharp Edges (2026)

Related Skills

ai-engineer, robotics-expert, research-engineer, embedded-systems