Depth Estimation of a Deformable Object via a Monocular Camera

Abstract

The depth estimation of the 3D deformable object has become increasingly crucial to various intelligent applications. In this paper, we propose a feature-based approach for accurate depth estimation of a deformable 3D object with a single camera, which reduces the problem of depth estimation to a pose estimation problem. The proposed method needs to reconstruct the target object at the very beginning. With the 3D reconstruction as an a priori model, only one monocular image is required afterwards to estimate the target object’s depth accurately, regardless of pose changes or deformability of the object. Experiments are taken on an NAO robot and a human to evaluate the depth estimation accuracy by the proposed method.

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

“…Many studies have been conducted on body tracking and motion analysis using depth images [20][21][22][23]. There are two methods for creating depth images: extracting features from two-dimensional images and inferring depth through learning [24][25][26][27] or shooting with a 3D depth camera [28][29][30]. The former method has disadvantages in that an additional process is required to extract and learn features of an image, it takes a lot of time, and the accuracy is low.…”

Unsupervised Monocular Depth Estimation Based on Residual Neural Network of Coarse–Refined Feature Extractions for Drone

“…Second, an innovative residual neural network of coarse-refined feature extractions for corresponding image reconstruction with long skip connections between corresponding layers in the neural network of coarse feature extractions and deconvolution neural network of refined feature extractions was proposed, through which the left approximate disparity map d l or right approximate disparity map d r was generated by the process that features outputted by the coarse residual neural network were multiplied by a fixed scale factor, and then the corresponding right image I r or left image I l was reconstructed based on the generated left approximate disparity map d l and input left image I l or the generated right approximate disparity map d r and input right image I r according to the method of bilinear interpolation. Finally, the left depth map z l or right depth map z r was generated based on input left image I l and corresponding reconstructed right image I r or the input right image I r and corresponding reconstructed left image I l according to the principle of binocular depth estimation that is shown in Figure 1 and Formulas (1)(2)(3)(4). According to some [30,31], the basic principle of binocular depth estimation is as follows: (a) Formulas (1,2) are derived according to the principle of triangle similarity.…”

“…It is known that image depth information can be easily obtained by camera binoculars, yet they are not suitable for drones due to high price and large size [2]. However, monocular depth estimation from a single image is an ill-posed problem in essence since an image has an infinite number of 3D scenes [3].…”

Monocular Depth Matching With Hybrid Sampling and Depth Label Propagation

“…Deep learning-based methods (Xu et al,2017) (Laina et al,2016) (Tateno et al,2017) (Tung et al,2017) (Zhou et al,2021) often use network architecture tuning to countervail complex deployment costs and huge computing costs. Based on the method of semantic segmentation information (Li et al,2018) (Wang et al,2018) (Liu et al,2015) (Jiang et al,2019), implicit geometric constraints are introduced in the training process. Through geometric transformation, view synthesis is used as a supervision signal to reduce the dependence on data.…”