What is BEV and what is Occupancy in the field of autonomous driving?
BEV is an abbreviation for Bird's Eye View, which means bird's-eye view. In the field of autonomous driving, BEV refers to a view of the scene from above the vehicle. BEV imagery can provide a complete view of the vehicle's surroundings, including the front, rear, sides, and top of the vehicle.
BEV images can be generated in a variety of ways, including:
- Use lidar: Lidar can directly measure the position of an object in three-dimensional space and then convert this data into a BEV image.
- Use a camera: The camera can generate a BEV image by calculating the perspective projection of the image.
- Use a hybrid sensor: A combination of lidar and camera can be used to generate BEV images for a more precise and complete view.
Occupancy Network is a deep learning method used by Tesla in autonomous driving. It is a 3D semantic occupancy awareness method that generates a three-dimensional occupancy mesh of the vehicle's surroundings from multi-view images.
Here's how Occupancy Network works:
- First, the Occupancy Network converts the input data from the multi-view image into a 3D feature space.
- The Occupancy Network then uses a deep neural network to learn the occupancy probability in this feature space.
- Finally, the Occupancy Network converts the occupancy probability into a 3D occupancy mesh.
Specifically, the Occupancy Network is mainly used in the autonomous driving system for the following tasks:
- Obstacle Detection: The Occupancy Network can be used to detect obstacles around vehicles, such as other vehicles, pedestrians, cyclists, and more.
- Path planning: The Occupancy Network can be used to generate vehicle paths and avoid obstacles.
- Vehicle control: The Occupancy Network can be used to control the speed and direction of the vehicle to ensure safe driving.
In the future, with the continuous development of autonomous driving technology, the Occupancy Network will play an even more important role in autonomous driving systems.
How to learn BEV and Occupancy Engineering?
The first is a solid foundation
Learning BEV and OCC requires a solid knowledge base including: linear algebra, calculus, probability theory, deep learning, computer vision, ubuntu operating system, C++, python, pytorch, matrix theory, and habit reading papers and blogs.
The second is data collection and processing capabilities
BEV and OCC require large amounts of data to train and validate algorithms, so data acquisition and processing capabilities are essential. Data collection can be done through sensors such as lidar, cameras, and millimeter-wave radars, and data processing requires cleaning, labeling, and enhancement of data to ensure the quality and availability of data.
In terms of data acquisition and processing, the following skills need to be mastered: sensor principles, data collection work, dataset framework development, data augmentation, etc., the versatility and applicability of open source datasets are often limited, so we need to customize the development according to our own engineering needs.
The third is the ability to develop algorithms
The algorithms of BEV and OCC need to be able to extract valid features from data 2D and 3D features, decouple feature heads, fuse features of multiple data, process time series information, and carry out effective modeling and inference. Algorithm development requires a solid foundation in mathematics, statistics, and machine learning, as well as some programming skills.
The fourth is the ability to scale the model
This is actually the most important point, drawing inferences from one another, combining existing data, referring to the direction of the frontier, putting forward challenging topics for yourself, and combining lane markings, timing information, 3D reconstruction, SLAM positioning and other technologies to improve the performance of your model.
How do you learn all of the above at the same time?
The 3D Vision Workshop and senior experts in autonomous driving jointly launched the online course "Panoramic Analysis and Practical Combat of BEV and Occupancy Networks". The course involves the training and generation of BEV (bird's-eye view) and Occupancy network models, aiming to train students to be able to not only apply occupancy network technology in the real environment, but also understand the application of NeRF in Occupancy, the application of lane markings, the application of world models, etc., as well as the origin, fusion, generation, calibration and optimization of data.
The core goal of the course is to help students quickly master the Occupancy technique, so that they can independently study relevant papers earlier and find their own project practice path. This not only significantly improves the competitiveness of individuals, but also provides a deeper understanding of which algorithms are best suited for practical applications – because we believe that only those that can actually be implemented are good algorithms.
To achieve this, we have carefully designed the syllabus to cover the key areas and the latest advancements in network technology, ensuring that participants have a comprehensive and in-depth understanding and mastery of this cutting-edge technology. The course outline is as follows:
What configuration is required?
- Hardware requirements: In order to successfully study and practice this course, it is recommended to have a computing system equipped with multiple graphics cards to cope with complex data processing and model training needs.
- For those who are temporarily unable to prepare such equipment, we recommend renting a cloud server to obtain the necessary computing resources. This course also recommends the use of an environment based on the Ubuntu operating system to ensure software compatibility and efficient workflows.
- In addition, actual data acquisition devices, such as cameras and Lidar, will greatly enhance the hands-on experience and give you a deeper understanding of the process of data acquisition and processing.
- If you don't have access to these devices, don't worry – we'll provide the necessary data sets to ensure that all participants can fully participate in the hands-on part of the course, regardless of your hardware.
Course features:
Hands-on: This course focuses on hands-on operations, guiding you from the data collection phase to explore the practical applications of BEV and Occupancy.
Comprehensive learning materials: Each chapter is equipped with detailed handouts and code examples to ensure that you have a comprehensive understanding of the core concepts and application techniques of BEV and Occupancy.
Deep Progressive Curriculum Framework: This course adopts an innovative deep progressive pedagogy, which is similar to building a "knowledge pyramid". Initially, we focused on the collection and interpretation of data to ensure that students had a strong foundation.
This will be followed by an in-depth look at the advanced techniques and adaptation strategies for BEV and Occupancy to ensure that students not only understand the theory, but also apply it flexibly.
Finally, the course will guide students to expand their horizons to the macro level, use the knowledge they have learned to think and apply independently in autonomous driving and other innovative fields, and realize the construction of a complete knowledge structure from basic to advanced to innovative applications. Explore the world of BEV and Occupancy together, not only learn theory, but also gain practical experience to broaden your horizons in autonomous driving and other fields.
Who listens to
- Academic Explorer: Whether you are an undergraduate, master's or doctoral student working in the field of computer vision and autonomous driving perception, this course will provide you with deep insights and practical skills for your research.
- Industry Professionals: If you are an algorithm engineer working in the field of computer vision and autonomous driving 2D/3D perception, this course will help you deepen your professional skills and master the most cutting-edge technologies in the industry.
- Forward-looking practitioners: For those who are working in the field of mass production and pre-research, and seeking to apply L2 to L4 autonomous driving technology, this course will be an ideal platform for you to improve your technology.
- Enthusiastic beginners: Even if you only have some understanding of computer vision, but have a great passion for the latest algorithms in the autonomous driving industry, there is a place for you to explore and grow together.
Regardless of your professional background or experience level, explore the wonderful world of autonomous driving, learn more about the cutting-edge technologies of the industry, and move towards the forefront of future technology trends together.
Harvest after school
- In-depth mastery of occupancy networks: Through this course, you will have a comprehensive understanding of occupancy networks, from theoretical foundations to programming practices, and gain an in-depth understanding of the development history, application scenarios, optimization strategies, and future development trends of this field.
- Efficient transfer of practice and application: The course content is designed to help you quickly apply the knowledge you have learned to scientific research and practical mass production projects, greatly saving time and costs, and accelerating the transformation process from theory to practice.
- Significant increase in industry competitiveness: After completing this course, you will have a clear competitive advantage in the field of autonomous driving. At the same time, the course provides you with the opportunity to network and collaborate with a wide range of industry professionals and learning partners.
- Quickly reach the professional level: After systematic learning, you will reach the level of perception algorithm engineer equivalent to 2 years of experience in a short period of time, and become an early researcher and engineer in the field of occupancy networks.
Not only will you enhance your professional skills, but you will also build a solid foundation for a career in the field of autonomous driving, which will help you become a leader in the industry.
Course Q&A
The Q&A of this course is mainly answered in the corresponding goose circle of this course, and students can ask questions in the goose circle at any time if they have any questions during the learning process.
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