The Essentials of 3D vs 2D Object Detection

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Nerd For Tech

9 min readFeb 15, 2024

Until recently, we mostly thought about computer vision from a two-dimensional perspective. In line with this, computer vision could be loosely defined as a field of artificial intelligence that attempts to recreate the ability of a human to look at an image and understand the image using techniques like 2D object detection.

Viewing the world through the eyes of algorithms. Source

However, since 2016, with the rise of three-dimensional deep learning, engineers have been using computer vision to mimic how humans perceive and understand the real world using techniques like 3D object detection. It doesn’t sound like much of a difference, right?

Well, let’s take a closer look. Consider taking a picture of a Tesla car with your phone. Once it uploads to your Google Drive, Google Photos’ algorithms analyze it. They swiftly classify it as a car, and you can run an image search with the keyword “cars.”

Then, consider test-driving a Tesla in a whole new way with Apple Vision Pro. You’d be in a virtual setting, feeling like you’re actually sitting in the driver’s seat. You could explore every feature and assess the car’s performance as if you were driving on the road.

What if we could take an immersive test drive of a Tesla using Apple Vision Pro? Source

Both of these applications use computer vision. The Google Photos example involves 2D object detection, while the immersive Tesla experience relies on 3D object detection. This distinction makes a huge difference! Let’s dive in and explore the essential differences between 3D and 2D object detection.

Understanding 2D Object Detection

Let’s start with: What is 2D object detection? Two-dimensional object detection is a computer vision technique that involves identifying and locating objects within two-dimensional images. Let’s say you pass an image containing various objects, such as a person, a car, and a dog, to a 2D object detection algorithm. The algorithm would analyze the image and identify the presence of these objects by drawing bounding boxes around them.

2D object detection being used to identify and locate a person and a dog.

The algorithm returns crucial information about each detected object. This typically includes providing coordinates for bounding boxes and labeling objects like “person,” “car,” and “dog” by category. Also, confidence scores may indicate the algorithm’s certainty in its predictions. These outputs allow other applications to interpret and use the identified objects within the image for different purposes like analyzing scenes, tracking objects, and making decisions.

So, how did 2D object detection come to be? Two-dimensional object detection has its roots in the 1970s with basic image processing techniques and has evolved through significant milestones.

The milestones of object detection evolution. Source

Here’s a summary of the milestones by decade:

1980s — Early attempts at object detection involve identifying shapes or edges within images.
1990s — Traditional methods like edge detection and template matching lay the foundation for object recognition tasks.
2000s — The Viola-Jones algorithm introduces real-time face detection using Haar-like features.
2010s — Deep learning, particularly CNNs, transformed object detection. Techniques like R-CNN, Faster R-CNN, YOLO, and SSD advanced detection accuracy and speed.
2020s — Ongoing advancements focus on improving the efficiency, robustness, and scalability of 2D object detection models.

How does 2D object detection work? The process behind 2D object detection involves teaching computers to recognize and locate objects in images. The first step of this process starts with annotating images using tools like Annotab Studio, where you can draw boxes around objects in pictures and label them.

Here’s a simple breakdown of how annotation works:

Data Collection — The first step is to collect a diverse range of images featuring the objects you intend to detect.
Uploading Images to an Annotation Tool — Choose an annotation tool that suits your needs, such as Annotab Studio, and upload the collected images or videos onto the platform.
Annotating — Then, annotators can click and drag to manually delineate bounding boxes around the objects within the images or video frames. Each bounding box indicates an object’s precise location and size.
Labeling Classes — Further, the annotators can assign appropriate labels or classes to each annotated object, specifying its identity (e.g., “car,” “person,” “cat”).
Export — Once the annotation is complete, they can export the annotated data into a format suitable for further processing and model training.

After labeling images, we train a model with this data. We teach the model to recognize objects by patterns and features. Our annotations guide this learning process. Once trained, the model can identify and locate objects in new images by running inferences.

Where can 2D object detection be applied? 2D object detection is widely applicable across various industries and scenarios. Take autonomous vehicles, for instance — they rely on 2D object detection to spot pedestrians, cyclists, cars, and even traffic signs, making driving safer for everyone. In retail, it helps keep track of inventory by counting and monitoring products on store shelves.

Using 2D object detection for inventory management. Source

Doctors also use it in healthcare for analyzing medical images like X-rays and MRIs, making it easier to spot any issues. Even in farming, it’s handy for monitoring crop health and spotting any lurking pests or diseases. Overall, 2D object detection offers practical solutions across different fields. It’s everywhere, quietly making life easier in all sorts of ways.

Now that we’ve understood 2D object detection. Let’s move on to 3D object detection.

Understanding 3D Object Detection

What is 3D object detection? 3D object detection extends the capability of 2D object detection to three-dimensional space. Instead of just drawing bounding boxes around objects like a person, car, or dog, the algorithm would also determine their spatial coordinates and orientations in the real world.

How did 3D object detection come to be? The rise of 3D deep learning and the availability of 3D datasets is how 3D object detection has successfully evolved across various milestones.

Here are some significant milestones:

2016 — Marked the beginning of deep learning’s application to 3D object detection, using neural networks to extract features from 3D data like point clouds and depth maps.
2017–2018 — Saw innovations in network architectures and data representations, including the introduction of PointNet, which directly processes point clouds.
2019 — Development of hybrid approaches like PointRCNN, combining point cloud processing with region proposals for better accuracy.
2020–2021 — Focus on improving real-time performance and handling sparse 3D data, with continued advancements in model design and data processing techniques.
2022 — Introduction of transformer-based models for 3D object detection, using attention mechanisms to enhance data processing.

How does 3D object detection work? 3D object detection works very similarly to 2D object detection. The difference in the process is that annotators must annotate additional information, such as depth or distance from the camera. Once annotated, the data is used to train a 3D object detection model. Advanced techniques like LiDAR (Light Detection and Ranging) and stereo vision are used alongside traditional image processing. These methods help the model understand the spatial layout of objects and accurately estimate their 3D properties.

Using LIDAR for 3D Object Detection. Source

Where can 3D object detection be applied? Like its 2D counterpart, 3D object detection finds applications across various domains. 3D object detection opens up new possibilities for perception and interaction. With respect to autonomous vehicles, it enables vehicles to perceive the environment in three dimensions for more precise navigation and obstacle avoidance. In robotics, it aids in object manipulation and scene understanding. Also, in augmented and virtual reality applications, 3D object detection facilitates real-time object tracking and interaction in immersive environments.

A robot using 3D object detection to pick and place objects. Source

Comparing 2D vs 3D Object Detection

We’ve understood the basics of 2D and 3D object detection. Next, let’s look at both of them side by side. The following breakdown clearly distinguishes between the key aspects of 2D and 3D object detection, from the types of data they process and their annotation requirements to their computational demands and application areas.

The Importance of Annotation Tools

It’s worth noting that annotation tools are essential in object detection. High-quality annotations directly impact the accuracy of object detection models. 2D and 3D annotation tools are essential for creating accurate and dependable object detection models. They help label large datasets and ensure that models trained with these datasets work well in real-world situations.

For 2D annotation, tools like Annotab Studio are popular for their user-friendly interfaces and capabilities to efficiently label objects within images or videos with precise bounding boxes.

When it comes to 3D object detection, the complexity increases as it involves annotating objects within three-dimensional spaces. A notable tool for 3D annotation is CVAT, which is designed to handle point clouds and images. This tool enables the precise labeling of objects in 3D space.

The Future of Object Detection

The future of object detection looks bright, thanks to concepts like open-world learning and multi-modal detection. Open-world learning aims for models to instantly learn and recognize new objects, a crucial feature in robotics. Multi-modal detection enhances accuracy by using various types of data, such as depth and thermal imagery.

An example of multi-model detection for autonomous driving. Source

Moreover, integrating object detection with augmented reality (AR) technology is unlocking new applications that merge digital and physical worlds, particularly in education and entertainment. As technology advances, object detection is set to play an increasingly significant role in our digital experiences, making them more immersive and intuitive.

Conclusion

We’ve covered the basics of 2D and 3D object detection and seen how they are applied in various industries. From identifying objects in photos to enhancing virtual experiences, these techniques are really making an impact.

Always remember to keep your eyes and ears open for the latest in AI. Tools like Annotab Studio are simplifying data annotation, and paving the way for even more breakthroughs.

Thanks for reading and learning with me. Farwell till our next dive deep.

FAQs

What are some key 2D object detection algorithms to start with? Popular options include YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), Faster R-CNN (Region-based Convolutional Neural Network), and RetinaNet. Each offers a unique balance of speed, accuracy, and complexity for different use cases.
What tools do I need to try out 3D object detection? To get started with 3D object detection, you’ll typically need hardware like LiDAR sensors or stereo cameras for capturing depth information. Software-wise, you’ll require frameworks like TensorFlow, PyTorch, or specialized libraries like Open3D, along with annotation tools such as CVAT (Computer Vision Annotation Tool) for labeling 3D data.

Resources

Here are some resources to check out if you are interested in object detection.

Glossary

LiDAR (Light Detection and Ranging): A remote sensing technology that uses laser pulses to measure distances to objects, commonly used for creating high-resolution maps and 3D models of environments.
Augmented Reality (AR): A technology that overlays digital information or virtual objects onto the real-world environment, enhancing the user’s perception of reality.
PointNet: A neural network architecture designed for processing point cloud data directly, commonly used in 3D object detection and classification tasks.
Stereo Vision: A technique used to perceive depth by comparing the visual information from two or more cameras or images taken from slightly different viewpoints.