Monocular Depth Estimation
Purchasing a 3D camera is a costly endeavour. Naturally, we can purchase two inexpensive cameras and use the stereo camera technique to estimate depth. However, there are five common methods for estimating depth that do not require a stereo camera.
(a) Monocular Depth Estimation: This method estimates depth using a single camera and a variety of computer vision techniques. supervised learning-based techniques, including monocular depth prediction with deep convolutional neural networks (CNNs) trained on massive datasets, are some of the more well-liked techniques.
(b) Focus-based Depth Estimation: This technique gauges depth by looking at how an image's focus varies. It is feasible to determine the depth of objects in a picture by examining the sharpness or blur of various locations in an image.
(c) Depth Estimation Based on Motion:This approach estimates depth based on the motion observed between successive frames in a video. By analyzing the optical flow or motion vectors, depth can be estimated using techniques such as structure from motion or visual odometry.
(d) LiDAR or Time-of-Flight (ToF) Sensors: LiDAR (Light Detection and Ranging) sensors or ToF (Time-of-Flight) cameras emit laser or infrared light and measure the time taken for the light to bounce back from objects in the scene. This information can be used to estimate dept.
(e) Depth from Defocus: This technique estimates depth by analyzing the defocus blur in an image. By capturing multiple images of the same scene with different focus settings, depth can be estimated based on the amount of defocus blur in each image.
Let's discuss indepth on monocular depth estimation. Applications for monocular depth estimation include robots, autonomous vehicles, augmented reality, and 3D reconstruction.
The goal of this computer vision method is to infer a scene's depth information from a single photograph. Put otherwise, it refers to the method of determining an object's distance inside a scene using only one camera viewpoint.
An essential first step in deriving scene geometry from 2D photos is depth estimate. With only one RGB image as input, the objective of monocular depth estimation is to infer depth information or forecast the depth value of each pixel. This example will demonstrate how to create a depth estimation model using a convolutional neural network and basic loss functions.
The method is difficult because it calls for the model to comprehend the intricate connections between the objects inthe scene and the associated depth information, which are influenced by several elements as texture, occlusion, and illumination. Compared to using stereo cameras or depth sensors, depth information is lost while recording a scene in a 2D image.
Below are four photographs taken of a yellow cylinder, with a distance of 18cm to 9cm from its base. One of the advantages of the Monocular DE is that it eliminates all background noises.
There is no obvious change in the color of the cylinder when the distance is short, but the background color becomes bluer when the distance is short. Therefore, the intensity of colour is a function of short distance and can be used to estimate depth.
(1) Distance of cylinder from base = 18cm
领英推荐
(2) Distance of cylinder from base = 15cm
(3) Distance of cylinder from base = 12cm
(4) Distance of cylinder from base = 9cm
It is noteworthy that although these methods can yield depth estimate without the need for a stereo camera, their accuracy and robustness may be restricted based on the particular application and environmental factors.
REFERENCES