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by yifanlu0227 · Agent Tool · ★ 430
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ChatSim Editable Scene Simulation for Autonomous Driving via LLM-Agent Collaboration Arxiv Video News [06/12/2024] 🔥🔥🔥 background rendering speed up! 3D Gaussian splatting is integrated as a background rendering engine, rendering 50 frames within 30s. [06/12/2024] 🔥🔥🔥 foreground rendering speed up! multiple process for blender rendering in parallel! rendering 50 frames within 5 minutes. Requirement Ubuntu version = 20.04 (for using Blender 3.+) Python = 3.8 Pytorch = 1.13 CUDA = 11.6 COLMAP or Metashape software (not necessary, we provide recalibrated poses) OpenAI API Key (you can also use other models' API from NVIDIA AI for free lunch) Installation First clone this repo recursively. Step 1: Setup environment Step 2: Install background rendering engine We offer two background rendering methods, one is in our paper, and another is . encodes the exposure time and achieves brightness-consistent rendering. is much faster (about 50 x
| Stars | 430 |
| Forks | 33 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 65.8970613029972/100 |
| Open Issues | 36 |
| Last Updated | 2024-12-11 |
| Created | 2023-12-02 |
| Platforms | python |
| Est. Tokens | ~20456k |
These tools work well together with ChatSim for enhanced workflows:
Looking for a ChatSim alternative? If you're comparing ChatSim with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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ChatSim is [CVPR2024 Highlight] Editable Scene Simulation for Autonomous Driving via LLM-Agent Collaboration. It is categorized as a Agent Tool with 430 GitHub stars.
ChatSim is primarily written in Python. It covers topics such as 3d, 3d-reconstruction, 3d-vision.
You can find installation instructions and usage details in the ChatSim GitHub repository at github.com/yifanlu0227/ChatSim. The project has 430 stars and 33 forks, indicating an active community.
The top alternatives to ChatSim on Agent Skills Hub include LLMCompiler, MedAgents, LiteWebAgent. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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