Supervised Monocular Depth Estimation Based on Machine and Deep Learning Models

Identificador:  TDX:4051

Handle: https://hdl.handle.net/20.500.11797/TDX4051

Autors:  Abdulwahab, Saddam

Resum:
Depth Estimation refers to measuring the distance of each pixel relative to the camera. Depth estimation is crucial for many applications, such as scene understanding and reconstruction, robot vision, and self-driving cars. Depth maps can be estimated using stereo or monocular images. Depth estimation is typically performed through stereo vision following several time-consuming stages, such as epipolar geometry, rectification, and matching. However, predicting depth maps from single RGB images is still challenging as object shapes are to be inferred from intensity images strongly affected by viewpoint changes, texture content, and light conditions. Additionally, the camera only captures a 2D projection of the 3D world. While the apparent size and position of objects in the image can vary significantly based on their distance from the camera. Consequently, this thesis attempts to contribute to two research lines in estimating depth maps (also known as depth images): the first line estimates the depth based on the object present in a scene to reduce the complexity of the complete scene. Thus, we developed new techniques and concepts based on traditional and deep learning methods to achieve this task. The second research line estimates the depth based on a complete scene from a monocular camera. We have developed more comprehensive techniques with a high precision rate and acceptable computational timing to get more precise depth maps.