Understanding Depth Estimation
Depth estimation is a complex task of measuring the distance of every pixel in an image relative to the camera. This process can be accomplished through a single image or multiple views of a scene. This method is highly useful in computer vision applications such as robot navigation, augmented reality, 3D mapping, and many others. The depth estimation process is made up of different sub-tasks such as feature extraction, disparity computation, and depth inference.
Two Main Methods of Depth Estimation
There are two main methods of depth estimation: monocular and stereo.
Monocular Depth Estimation: This method involves using only one image to determine the depth of pixels in that image. Through the use of various algorithms such as depth from focus, depth from defocus, or depth from motion, the depth of the scene can be estimated.
Stereo Depth Estimation: This method involves using multiple views of the scene to estimate depth. A stereo system consists of two cameras that capture corresponding images of the scene. The distance between the two cameras is known as the baseline, which is used to compute the disparity between the two images. Disparity is the difference in pixel location between corresponding points in the two images. The disparity is then used to compute the depth using the triangulation process.
Traditional Methods of Depth Estimation
Traditional methods of depth estimation use multi-view geometry to find the relationship of images. The process involves three main steps:
- Feature Detection: Identifying features in both images using techniques such as Scale-Invariant Feature Transform (SIFT).
- Feature Matching: Determining the correspondence between features in both images. This is usually accomplished through a variety of algorithms, including nearest neighbor matching.
- Disparity Computation: This involves computing the disparity between corresponding features in both images using algorithms such as dynamic programming or block matching. The disparity is then used to compute the depth of the scene using triangulation.
Newer Methods of Depth Estimation
Newer methods of depth estimation can directly estimate depth by minimizing the regression loss. These methods do not require feature detection or matching, making them faster and more accurate. They operate by taking a single image and estimating the intrinsic or extrinsic parameters of the camera. Using this information, the depth of each pixel is computed through the minimization of a loss function.
Another newer method of depth estimation involves learning to generate a novel view from a sequence. This method uses deep learning models such as Convolutional Neural Networks (CNN) to infer depth information from a series of images. This approach is known as Structure-from-Motion (SfM) and is a relatively new deep learning approach to depth estimation.
Popular Benchmarks for Evaluating Depth Estimation Models
The most popular benchmarks for evaluating depth estimation models are KITTI and NYUv2. The KITTI dataset consists of a collection of street-level images taken from a moving vehicle. The images contain a large amount of variation in lighting and weather conditions. The NYUv2 dataset consists of indoor scenes such as bedrooms, kitchens, and living rooms. The dataset contains complex scenes with a wide range of lighting and texture conditions.
Models for depth estimation are typically evaluated according to an RMS (Root Mean Square) metric. This metric measures the difference between the true depth and the estimated depth. Models with lower RMS errors are considered more accurate.
Depth estimation is a fundamental task in computer vision that has many different applications. Two main methods of depth estimation are monocular and stereo. Traditional methods use multi-view geometry, and newer methods can directly estimate depth. There are several popular benchmarks for evaluating depth estimation models, and these models are typically evaluated using the RMS metric. As technology advances, we can expect to see depth estimation playing an increasingly important role in computer vision applications.
