Auto-magically Annotate Point Cloud Data Like a Pro, with BasicAI Cloud Auto 3D Annotation Tool
Picture this...
You're an experienced data annotator, hunched over your workstation, carefully clicking to draw three points around a car object in a dense 3D point cloud dataset. You create a 3D cuboid, but it's not quite right. It's loose, and now you have to adjust it several times to make it snugly fit the object. This tedious and repetitive process is starting to wear you down, and you can't help but make mistakes as your focus wanes.
This is the reality for countless data annotators working with 3D point cloud data. But what if there was a solution that could vastly improve efficiency and reduce the risk of errors in this painstaking process?
Manually Building a Perfect 3D Dataset is Time-consuming and Error-prone
The rise of 3D LiDAR sensors has spurred the need for high-quality annotated 3D LiDAR point cloud data in object recognition and tracking models for autonomous driving. At its core, annotating point cloud data consists of constructing 3D cuboids and projecting to 2D images (in sensor fusion data).
Creating an impeccable annotated dataset can be an arduous and error-prone endeavor. Manual annotation often demands significant effort, expertise, and resources, making it both tedious and susceptible to inaccuracies. With the ever-growing demand for high-quality annotated data, AI engineers and annotators must seek more efficient and cost-effective solutions that deliver rapid, accurate annotations with minimal errors.
Enter the auto-annotate 3D LiDAR Point Cloud feature on the BasicAI Cloud. This innovative platform harnesses state-of-the-art machine learning algorithms to streamline and automate the annotation process, significantly cutting down the time and costs associated with manual labor. By using the auto-annotate feature, AI trainers can swiftly generate top-tier, precision-annotated point clouds, expediting the development and deployment of dependable and safe vision AI applications, such as autonomous driving systems, with confidence.
10 Reasons Why BasicAI Cloud Automated 3D Annotation Can Change the Game
1. 82x Faster: Manual Annotation vs Auto Annotation
For any data annotation business, both speed and accuracy are the key. 3D LiDAR point cloud data annotation demands a highly skilled team to ensure top-notch efficiency and quality. Enter auto annotation, the game-changing solution that leaves manual annotation in the dust.
Check out this head-to-head comparison below: While a manual annotator takes a painstaking 20 seconds to draw 5 3D cuboids for "Car" objects (with slight inaccuracies), another annotator armed with BasicAI Cloud's auto annotation tool pulls off a whopping 41 voxel-perfect "Car" cuboids during the same time frame! Plus, the cuboids dynamically move in sync with objects across 3D point cloud frame series (that means 410 cuboids are created), eliminating the need to tediously draw cuboids and connect objects in separate frames.
The verdict? Auto annotation on BasicAI Cloud skyrockets the annotation efficiency of 3D LiDAR point cloud data by an astounding 82x*!
* Note: The sensor fusion dataset used in this video for comparison purposes is from the open-source PandaSet. We only compare the number of 3D cuboids created for objects at the same time using manual annotation methods and the BasicAI Cloud automatic annotation tool. It is essential to note that the automatic annotation results often require manual fine-tuning to ensure accuracy. The fusion data automatic annotation and object tracking model on BasicAI Cloud can achieve an accuracy rate of up to 98%. The comparison results provided in this video are for reference only. The actual improvement in efficiency may vary depending on the specific dataset used.
2. Larger Point Cloud Dataset Support (over 150m points in 300 frames)
As the self-driving car industry continues to evolve, we're witnessing a paradigm shift in the hardware and software that powers these vehicles. Thanks to falling chip prices, increased computational capabilities, reduced energy consumption, and longer battery life, we can expect a rapid adoption of high-resolution cameras and 3D sensors, consequently enhancing the visual perception capabilities of autonomous vehicles.
To keep pace with these advancements, point cloud datasets must also level up, offering higher resolution, more points, and increased frame rates. BasicAI Cloud is poised to be a game-changer in this space. This robust platform has been designed to handle larger point cloud datasets, effortlessly scaling to accommodate the growing data volumes associated with LiDAR projects. The platform's ability to process and display vast point clouds without compromising performance or stability sets it apart from the competition.
BasicAI Cloud's impressive support for over 150 million LiDAR points and 300 consecutive frames in a single project makes it the perfect solution for businesses looking to stay ahead of the curve in the autonomous driving revolution. Don't get left behind—experience the power of BasicAI Cloud today.
3. Near-to-perfect Accuracy
REMEMBER: In the realm of AI model training, finding a one-size-fits-all pre-trained model with 100% accuracy remains an elusive goal. However, the auto-annotation tool on BasicAI Cloud pushes the boundaries of what's possible by combining minimal human intervention with exceptional accuracy. The secret behind this powerful tool lies in the meticulous fine-tuning carried out by BasicAI's team of technical experts.
Boasting an impressive 98%+ accuracy rate for auto-annotation of 3D LiDAR point cloud data, BasicAI Cloud sets a new industry standard, outperforming nearly all competitors in the market. The platform's near-to-perfect accuracy empowers users to confidently rely on its auto-annotation capabilities, ultimately streamlining the entire model training process.
Experience the remarkable accuracy of BasicAI Cloud's 3D LiDAR point cloud auto-annotation tool and elevate your AI projects to new heights of precision and efficiency.
4. Parameter Tuning
The world of autonomous driving is diverse, and so are the requirements for AI-powered object detection and tracking models. BasicAI Cloud understands this need for flexibility and has designed a groundbreaking feature to address it: Parameter Tuning. This innovative functionality embedded within BasicAI Cloud's pre-trained model allows users to customize their setup by adjusting confidence levels (from 0.0 to 1.0) and selecting specific Classes (or targeted objects) for precise and accurate auto-annotation of 3D LiDAR point cloud data.
Tailored specifically for autonomous driving scenarios, Parameter Tuning empowers AI engineers to focus on training models for their unique use cases. For instance, if an engineer aims to develop a model that detects "cars" on a road without considering bicycles or buses, they can simply choose the "Car" Class in the AI Annotation settings before running the prediction. With a click of the "Apply and Run" button, BasicAI Cloud works its magic, automatically labeling all cars in the dataset using 3D cuboids.
Harness the power of Parameter Tuning with BasicAI Cloud and streamline your autonomous driving model development process with ultimate precision and adaptability.
5. Tracking Annotated Objects in Frame Series
Training an object tracking model requires a 3D LiDAR point cloud dataset with consecutive frames, and BasicAI Cloud rises to the challenge by supporting an impressive 300 frames within a dataset. While auto-annotating objects frame by frame is a significant advantage, connecting the same object across multiple frames can still pose a challenge.
To address this, BasicAI Cloud's auto-annotation tool introduces a dynamic object tracking feature. This powerful capability allows for automatic copying and movement of cuboids in sync with the motion of objects across frames, streamlining the annotation process and ensuring consistent tracking throughout the entire frame series.
Unlock seamless object tracking with BasicAI Cloud's 3D point cloud frame series annotation and simplify the development of your object tracking models, while maintaining accuracy and efficiency throughout the process.
6. Sensor Fusion Data Support
In the quest to develop machines that perceive the world as humans do, AI scientists have turned to sensor fusion technology in autonomous driving. By combining data from multiple sensors such as cameras, LiDAR, and radar, vehicles can achieve a more comprehensive and reliable understanding of their surroundings. Recognizing the immense potential of sensor fusion, BasicAI Cloud has expanded its capabilities to support not only 3D point cloud data but also 2D & 3D camera-LiDAR fusion data.
BasicAI Cloud offers a seamless, integrated view of all layers, merging 3D LiDAR and 2D camera perspectives. This holistic approach streamlines the annotation process as 3D point cloud annotations (3D cuboids) are automatically mapped to corresponding 2D images (2D cuboids). This powerful feature enables users to export both 2D and 3D results in a single step, simplifying annotation and segmentation tasks.
Leverage BasicAI Cloud's support for 2D & 3D sensor fusion data to elevate your autonomous driving projects and gain valuable insights from a comprehensive, multi-sensor perspective.
7. Teamwork Management: Workflow, Performance and Roles
For collaborative annotation projects, a good platform should have smooth task management, progress tracking and performance management system.
BasicAI Cloud simplifies workflow optimization, allowing you to track tasks and gain deeper insights into your team's productivity. Identify areas for improvement and enhance your processes with ease. The Task Flow feature can be helpful in handling tasks with large amounts of data by improving collaboration, increasing productivity, and tracking progress. Additionally, our Role & Permission management enables every member of your annotation team to get the right access and involvement. The Performance dashboard provides a real-time view of member performance and project progress.
At BasicAI, we believe that teamwork should be smooth, stress-free, and efficient. That's why we've designed our platform to offer a seamless annotation experience for you and your team.
8. Ontology-Powered Label System
Ontology is a structured way of describing everything in the world, including three elements: Class, Relation and Properties. In Machine Learning, Ontology is increasingly used to provide ML models based on similarity analysis and scenario knowledge. Unlike isolated, label-based definitions with limited scalability and potential duplication issues, Ontology-based definitions establish interconnected relationships between objects. This enables advanced features like scenario search, Ontology fusion, and Ontology recommendation through labeling of relations.
BasicAI Cloud 3D LiDAR point cloud annotation is powered by Ontology system. You can build your own Ontology structure for your project. After defining classes and properties in Ontology Center, you can easily search for scenarios such as “Chage Lane”. The Ontology Center can also deduce new annotations based on rules between classes, properties and relations. As the amount of Ontology data increases, the Ontology Center can also recommend better-performing Ontology models in different domains.
9. Easy Review (Quality Check)
In the ever-evolving world of autonomous systems, timely and accurate analysis of 3D LiDAR point cloud data is crucial. BasicAI Cloud's auto-annotation toolset now offers an efficient and user-friendly solution to review single frame point cloud data with ease. By utilizing the right side bar, users can access three distinct views: front, rear, and side perspectives. This comprehensive display allows for real-time adjustments to the 3D cuboid, ensuring precise annotations.
Moreover, when dealing with point cloud frame series data, the toolset's unique feature enables users to view all three perspectives of an object across multiple frames in a single window. This streamlined approach optimizes the review process, making it easier than ever to refine annotations for even the most complex datasets. Experience unparalleled control and precision with BasicAI Cloud's 3D LiDAR point cloud auto-annotation toolset, and elevate your data analysis to new heights.
10. Next GEN Features, at 0 Cost!
Step into the future with BasicAI Cloud, the ultimate free playground for tech aficionados! Unchain your creativity and dive into the world of limitless annotation capabilities, where every user gets VIP treatment. No more FOMO – with BasicAI Cloud, you'll enjoy access to all the rad annotation tools without any pesky limitations.
Here's the scoop: each registered user scores 5 seats, a whopping 200GB of storage, and 10,000 model calls. What does that mean for you? It's time to auto-annotate like a boss, with the ability to process 10,000 frames of point cloud data, 2D & 3D sensor fusion data, or images. So, polish your tech goggles and prepare to be dazzled by this game-changing free annotation platform. BasicAI Cloud is the go-to destination for those who live and breathe innovation!
No matter when you need assistance, don't hesitate to contact us. Our support team is available around the clock, ready to provide you with the information and guidance you require. Trust in BasicAI Cloud as your reliable partner, committed to delivering unparalleled customer service and empowering you to reach new heights in your endeavors.
Why Nailing the 3D Dataset is Crucial?
Remember, the quality of your annotations has a direct impact on your vision AI model's performance.
A top-notch 3D LiDAR Point Cloud dataset is vital for vision AI applications, particularly in safety-sensitive fields like autonomous driving. High-quality annotations facilitate accurate object detection, classification, and tracking, ensuring that AI systems correctly perceive and interpret their surroundings. Accurate annotation of point clouds is key to capturing the depth and spatial information of various objects, such as vehicles, pedestrians, and obstacles. This level of detail is essential for AI systems to make informed decisions, predict the behavior of other road users, and devise safe and efficient navigation routes. In autonomous driving scenarios, even minor mistakes in annotated data can result in catastrophic consequences, including accidents and loss of human life. Thus, a meticulously annotated 3D LiDAR Point Cloud dataset is paramount for developing and validating robust, reliable vision AI systems that prioritize safety and efficiency.
