OccWorld

Visit Tool

OccWorld is an Open Source AI Agents & Automation tool that provides a 3D world model for autonomous driving. It enables forecasting of future scene evolutions and ego movements.

Claim this tool

1View

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is OccWorld?

OccWorld is an open-source 3D world model specifically designed for autonomous driving applications, presented at ECCV 2024. This tool allows for the joint modeling of 3D scene evolutions and ego movements, crucial for developing advanced autonomous systems. It can forecast the movements of surrounding agents and future map elements like drivable areas, demonstrating an understanding of the scene beyond mere memorization. OccWorld integrates with various 3D occupancy models such as SelfOcc, TPVFormer, and SurroundOcc, offering a scalable solution for large-scale training and paving the way for interpretable end-to-end large driving models. The project provides code for visualization, training logs, and a pretrained model, making it a valuable resource for researchers and developers in the autonomous driving domain.

Best used for

Ideal for developers and data scientists who need to research and develop advanced autonomous driving systems, forecast future scene evolutions, and understand complex 3D scene dynamics. Especially valuable for those working on interpretable end-to-end large driving models and integrating with existing 3D occupancy frameworks.

Common actions

model 3D environments

forecast driving scenarios

develop autonomous systems

research AI for driving

open-sourcedeepfakelow-code/no-codeautomated workflowcollaborationworkflowsface swappinggithub copilot"AI Agents"

Capabilities

Key features

3D world model
Autonomous driving
Scene evolution forecasting
Ego movement forecasting
Pretrained models

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of data does OccWorld use for training?

OccWorld is designed to work with various 3D occupancy data, including self-supervised (SelfOcc), LiDAR-collected (TPVFormer), and machine-annotated (SurroundOcc) data. Users need to prepare their dataset, such as nuScenes, with specific semantic occupancy information for training.

What are the hardware requirements for running OccWorld?

The documentation specifies that training the VQVAE and OccWorld models, as well as evaluation, can be performed on an RTX 4090 GPU with 24GB of memory. This indicates a need for high-performance computing resources.

Can OccWorld predict new vehicles entering the sight?

While OccWorld excels at forecasting movements of surrounding agents and map elements, the project notes that it currently struggles to forecast new vehicles entering the sight if they are absent from the initial inputs. This is a known limitation.

Trending

Subcategories trending in AI Agents & Automation

AI Frameworks & Infra Chatbots & Conversational AI Workflow Agents Personal Assistants RAG & Document AI Voice Agents

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce