When it comes to in-house 3D LiDAR annotation, the biggest challenges most teams face are budgetary constraints, slow turnaround and inconsistent quality.
Ideally, you want any annotation tasks managed internally to ensure full control and data security (especially for sensitive datasets). But with such technical complexities, in-house tasking will likely result in slower development cycles and weaker model performance. That’s why AI and computer vision teams today choose to outsource their 3D LiDAR annotation needs.
Key Takeaways
- In-house 3D LiDAR annotation faces challenges like high costs, slow turnaround, and inconsistent quality.
- Outsourcing 3D LiDAR annotation reduces costs and allows teams to focus on AI development rather than infrastructure.
- Specialized annotation teams ensure improved quality and accuracy through uniform guidelines and quality assurance protocols.
- Outsourcing provides scalability, enabling quick adjustments to fluctuating workloads without hiring or layoffs.
- Quality 3D LiDAR annotation is vital for autonomous systems, impacting safety, reliable prediction, and competitive edge.
Table of contents
Why Outsource 3D LiDAR Annotation?
Cost-Effectiveness
As mentioned, one of the leading challenges of in-house 3D LiDAR annotation is high costs. Most AI teams can’t afford to fund this part of the project internally without bloating the budget.
Should you decide to handle annotation in-house, you would start with infrastructure setup. That includes sourcing high-performance computing hardware and specialised software licenses, all of which can be really costly.
Outsourcing through oworkers and such platforms helps you avoid the overall infrastructure costs. It also frees up your internal team of AI engineers to focus on what they are good at (algorithm tuning and model development).
Improved Quality
Another reason to consider outsourcing 3D LiDAR annotation is to ensure quality (in terms of accuracy).
Internal AI teams (mostly made of engineers) might be very good at developing models and algorithms, but that doesn’t mean those skills are transferable to annotation. Outsourcing gives you access to specialised teams with skills and experience in 3D LiDAR annotation. These teams follow strict, uniform guidelines designed to minimise discrepancies in how different annotators label complex/ambiguous scenes.
In addition, most of these 3D LiDAR annotation service providers employ multi-layered quality assurance protocols. They are specialised in annotation, which means all their focus would be in getting it right all the time.
Scalability
Another big challenge with in-house 3D LiDAR annotation is scalability. You probably already know that LiDAR sensors can generate enormous datasets within very short times (millions of points per second). That said, the workload can fluctuate dramatically – one day you might need 10 annotators, the next 20 and later it could drop to just 5.
Outsourcing allows you to scale up and down quickly to handle the fluctuating workload without having to hire and fire staff every time.
Can you imagine hiring and training a whole team, letting a few go a week later and then needing them again just a few weeks after? Someone might suggest keeping the entire team around for the entirety of the project. But the unnecessary wages are what bloats budgets.
Faster Turnaround Times
Another reason why it is wise to outsource 3D LiDAR annotation is the faster turnaround times. It accelerates your model development cycles in several ways.
For starters, it gives you immediate access to specialized expertise. You can skip all the struggles of interviewing and training members for the team. Once you outsource, the provider gives you access to an already skilled team ready to go.
Also, outsourcing allows for rapid scaling. The provider can increase or reduce the number of annotators based on your needs. That ensures you can add resources during high-demand phases and reduce them when it is a little quieter. It goes without mentioning, but it allows that, minus all the logistical bottlenecks of hiring or layoffs.
Why Quality 3D LiDAR Annotation is Essential for Autonomous Systems
As far as outsourced 3D LiDAR annotation is concerned, quality assurance is an aspect that keeps getting mentioned. But why is quality a non-negotiable for autonomous systems?
Put simply, quality in 3D LiDAR annotation is important because it enables autonomous systems to accurately perceive their surroundings.
This annotation process is basically the backbone of the entire system. If not done properly, things like real-time navigation and object recognition wouldn’t be reliable or would be impossible (in extreme cases).
Here is a summary of why quality annotation services are important:
- Safety – quality 3D LiDAR annotation reduces the risk of false positives and misrepresentation (such as misidentifying stop signs or pedestrians). That is crucial for avoiding accidents.
- Reliable prediction – accurate 3D data facilitates reliable prediction, which is essential for things like path planning and decision-making.
- Competitive edge – the market for autonomous systems is quite competitive. High-quality annotation services gives your business a competitive edge as it ensures your systems consistently operate safely and reliable in diverse environments.
Conclusion
In-house 3D LiDAR annotation presents plenty of challenges, including high costs, reduced quality, slower development cycles and limited scalability. This is why many AI and computer vision teams are opting to outsource.
Outsourcing ensures high-quality data sets, faster turnaround times and easier scalability. It also gives you a competitive edge – you can access quality annotation services cost-effectively.
