In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research.

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Depth estimation is a challenging task of 3D reconstruction to enhance the accuracy sensing of environment awareness. This work brings a new solution with a set of improvements, which increase the quantitative and qualitative understanding of depth maps compared to existing methods. Recently, convolutional neural network (CNN) has demonstrated its extraordinary ability in estimating depth maps from monocular videos. However, traditional CNN does not support topological structure and they can work only on regular image regions with determined size and weights. On the other hand, graph convolutional networks (GCN) can handle the convolution on non-Euclidean data and it can be applied to irregular image regions within a topological structure. Therefore, in this work in order to preserve object geometric appearances and distributions, we aim at exploiting GCN for a self-supervised depth estimation model. Our model consists of two parallel auto-encoder networks: the first is an auto-encoder that will depend on ResNet-50 and extract the feature from the input image and on multiscale GCN to estimate the depth map. In turn, the second network will be used to estimate the ego-motion vector (i.e., 3D pose) between two consecutive frames based on ResNet-18. Both the estimated 3D pose and depth map will be used for constructing a target image. A combination of loss functions related to photometric, reprojection and smoothness is used to cope with bad depth prediction and preserve the discontinuities of the objects. In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly KITTI and Make3D datasets along with a reduction of 40% in the number of trainable parameters compared to the state of the art solutions. The source code is publicly available at https://github.com/ArminMasoumian/GCNDepth.git

PURPOSE: To assess the changes in balance function in children with cerebral palsy (CP) after two weeks of daily training with personalized balance games. METHODS: Twenty-five children with CP, aged 5 to 18 years were randomly selected for experimental or control groups. Over a period of two weeks, all participants received 8–9 game sessions for 15–20 minutes, totaling 150–160 minutes. The experimental group used personalized balance games available from the GAmification for Better LifE (GABLE) online serious gaming platform. Children from the control group played Nintendo Wii games using a handheld Wii Remote. Both groups received the same background treatment. Recorded outcome measures were from a Trunk Control Measurement Scale (TCMS), Timed Up & Go Test (TUG), Center of Pressure Path Length (COP-PL), and Dynamic Balance Test (DBT). RESULTS: After two weeks of training in the experimental group TCMS scores increased by 4.5 points (SD = 3.5, p< 0.05) and DBT results increased by 0.88 points (IQR = 1.03, p< 0.05) while these scores did not change significantly in the control group. Overall, TUG and COP-PL scores were not affected in either group. CONCLUSION: This study demonstrates improvement of balancing function in children with CP after a two-week course of training with personalized rehabilitation computer games.