Object depth estimation from single image

• Main Author: Long, Zhongtian
• Other Authors: Mao Kezhi
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/167156
• Institution: Nanyang Technological University

With the rapid development of computer vision technology, depth estimation is widely used in autonomous driving. Combined with object detection, the effect of pseudo-laser detection or three-dimensional reconstruction can be achieved; Combined with semantic segmentation, it can be extended from 2D to 3D to obtain semantic and depth information of pixels, such as lane line detection; In addition, depth estimation can also be used for general obstacle detection[1]. Therefore, depth estimation is an important visual task in autonomous driving. The method of monocular[2] depth estimation is to estimate the depth from a single or a series of visible light photos taken simultaneously in the same scene. It also includes methods based on monocular vision, stereo matching, multi-view stereoscopic and 3D reconstruction. This dissertation first introduces some basic technology and several commonly used methods for depth estimation. Then, the paper presents a comprehensive study of monocular depth estimation using the Monodepth2 model[25]. The Monodepth2 model is explained in detail, including its network structure, components, and loss function. The environment setup and datasets used for pre-training the model on the Cityscapes dataset[28] and testing and fine-tuning it on the KITTI dataset[27] are described in the experimental section. This study evaluates the model using acceptable depth estimation indices as MSE, MAE, and Abs.rel. The outcomes of this experiment are evaluated using three different training techniques: monocular training, stereo training, and monocular plus stereo training[25]. In the end, it is discovered that the experimental results that have been examined and replicated are nearly identical to the original experimental results. Based on the experimental findings, a direction and method for enhancing the Monodepth2 model in future studies are suggested. Overall, this study offers insightful information about monocular depth estimation using the traditional Monodepth2 method.