Monocular Depth Estimation

Author

Abhishek Kumar Pandey
May 21, 2024

Monocular Depth Estimation

Easy:

So, you know how we can see the world around us, right? Like, we can see how far away things are from us, and how big or small they are. That’s because our brains are super good at understanding something called “depth”.

But, did you know that computers and robots can’t see the world like we do? They need special help to understand depth. That’s where “monocular depth estimation” comes in!

“Monocular” means “one eye”. So, monocular depth estimation is like trying to figure out how far away things are from a picture taken with just one camera (or eye). It’s like trying to guess how far away a toy car is from you just by looking at a photo of it.

Imagine you’re playing with a toy car on the floor. You take a picture of it with a camera. Now, if you show that picture to a computer, it wouldn’t know how far away the car is from the camera. It’s like the computer is blind to depth!

But, with monocular depth estimation, the computer can use special tricks to try to figure out how far away the car is. It’s like the computer is trying to guess the distance by looking at the picture really closely.

Here’s how it works:

  1. The computer looks at the picture and finds things like edges, lines, and shapes.

  2. It uses those edges, lines, and shapes to make an educated guess about how far away the car is.

  3. The computer then creates a special map, called a “depth map”, that shows how far away everything in the picture is.

It’s like magic! The computer can take a flat picture and turn it into a 3D world, just by using its brain (algorithms) and some clever tricks!

So, monocular depth estimation is like a superpower that helps computers and robots understand the world in 3D, even when they only have one “eye” (camera) to look at it with!

Near and Far

Moderate:

Monocular depth estimation is a technique used in computer vision to determine the depth or distance of objects in a scene captured by a single camera (or “eye”). Unlike stereo vision, which uses two cameras to capture the same scene from slightly different angles, monocular depth estimation relies solely on a single viewpoint. This makes it particularly useful in applications where multiple cameras are not feasible or practical.

The challenge with monocular depth estimation lies in the fact that we perceive depth through our eyes due to cues such as perspective, texture gradients, shading, and motion parallax, among others. However, without additional information (like the second view provided by stereo vision), accurately estimating depth becomes significantly harder. Despite this, advancements in machine learning and deep learning techniques have made significant strides in improving the accuracy of monocular depth estimation.

Here’s a simplified explanation of how monocular depth estimation works:

  1. Feature Detection: The first step involves detecting features within the image that can provide depth information. These could be edges, corners, or any distinctive points that can be matched across frames or between different parts of the image.

  2. Learning Depth Cues: Machine learning models are trained to recognize various depth cues present in images. This training process involves feeding the model thousands or even millions of images along with their known depth information. Through this process, the model learns to associate certain visual patterns with specific depths.

  3. Estimation: Once the model has been trained, it can analyze new images and estimate the depth of objects within those images. This is done by identifying similar patterns to those it was trained on and applying the learned associations to estimate depth.

  4. Post-processing: After the initial depth estimates are generated, post-processing techniques may be applied to refine these estimates. This could involve smoothing the depth map to remove noise or applying algorithms to correct for common errors.

  5. Applications: Monocular depth estimation finds use in a variety of applications, including autonomous vehicles for obstacle detection and navigation, virtual reality for creating immersive environments, and robotics for tasks requiring spatial awareness.

Despite the challenges inherent in estimating depth from a single viewpoint, monocular depth estimation remains a vibrant area of research, with ongoing efforts to improve accuracy and applicability across different scenarios.

Hard:

Monocular depth estimation is a computer vision technique that involves estimating the depth information of a scene from a single 2D image. In other words, it’s a way to infer the 3D structure of a scene from a single image, without using any additional information such as stereo vision or structured light.

The goal of monocular depth estimation is to produce a depth map, which is a 2D representation of the scene where each pixel value corresponds to the distance of the corresponding point in the scene from the camera. This depth map can be used for various applications such as:

  1. 3D reconstruction: Creating a 3D model of the scene from the estimated depth map.

  2. Scene understanding: Understanding the layout and structure of the scene, such as identifying objects, surfaces, and obstacles.

  3. Robotics and navigation: Enabling robots and autonomous vehicles to navigate and interact with their environment.

  4. Augmented reality: Enhancing the user experience by providing more accurate and realistic AR effects.

Monocular depth estimation is a challenging problem because it’s difficult to infer depth information from a single 2D image. The technique relies on various cues and assumptions to estimate depth, including:

  1. Geometric cues: Using the geometry of the scene, such as lines, edges, and shapes, to infer depth.

  2. Shading and texture: Analyzing the shading and texture patterns in the image to estimate depth.

  3. Atmospheric cues: Using the effects of the atmosphere, such as haze and fog, to estimate depth.

  4. Motion cues: Analyzing the motion of objects in the scene to estimate depth.

There are several approaches to monocular depth estimation, including:

  1. Traditional computer vision methods: Using hand-crafted features and algorithms to estimate depth.

  2. Deep learning-based methods: Using convolutional neural networks (CNNs) to learn features and estimate depth.

  3. Hybrid approaches: Combining traditional computer vision methods with deep learning-based methods.

Some popular deep learning-based architectures for monocular depth estimation include:

  1. Depth from Mono (DfM): A CNN-based architecture that uses a encoder-decoder structure to estimate depth.

  2. Monodepth: A CNN-based architecture that uses a ResNet-based encoder and a decoder with skip connections to estimate depth.

  3. DenseDepth: A CNN-based architecture that uses a dense connection-based encoder and a decoder with skip connections to estimate depth.

Monocular depth estimation has many applications in computer vision and robotics, including:

  1. Autonomous vehicles: Enabling self-driving cars to navigate and understand their environment.

  2. Robotics: Enabling robots to navigate and interact with their environment.

  3. Virtual and augmented reality: Enhancing the user experience by providing more accurate and realistic AR effects.

  4. Surveillance and monitoring: Enabling surveillance systems to understand and track objects in the scene.

However, monocular depth estimation is still an active area of research, and there are many challenges and limitations to overcome, including:

  1. Limited accuracy: Monocular depth estimation is still not as accurate as stereo vision or other depth sensing techniques.

  2. Occlusions and shadows: Handling occlusions and shadows in the scene can be challenging.

  3. Variability in lighting: Changes in lighting can affect the accuracy of monocular depth estimation.

  4. Domain shift: Adapting to new environments and domains can be challenging

A few books on deep learning that I am reading: