Dataset Information

Online supervised attention-based recurrent depth estimation from monocular video.

ABSTRACT: Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general. One of the way to improve quality of existing methods is to utilize temporal information from frame sequences. In this paper, we study intelligent ways of integrating recurrent block in common supervised depth estimation pipeline. We propose a novel method, which takes advantage of the convolutional gated recurrent unit (convGRU) and convolutional long short-term memory (convLSTM). We compare use of convGRU and convLSTM blocks and determine the best model for real-time depth estimation task. We carefully study training strategy and provide new deep neural networks architectures for the task of depth estimation from monocular video using information from past frames based on attention mechanism. We demonstrate the efficiency of exploiting temporal information by comparing our best recurrent method with existing image-based and video-based solutions for monocular depth reconstruction.

SUBMITTER: Maslov D

PROVIDER: S-EPMC7924529 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Online supervised attention-based recurrent depth estimation from monocular video.

Maslov Dmitrii D Makarov Ilya I

PeerJ. Computer science 20201123

Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general. One of the way to improve quality of existing methods is to utilize temporal information from frame ...[more]

PMID: 33816967

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Project description:ObjectivesReliable determination of cochlear implant electrode positions shows promise for clinical applications, including anatomy-based fitting of audio processors or monitoring of electrode migration during follow-up. Currently, electrode positioning is measured using radiography. The primary objective of this study is to extend and validate an impedance-based method for estimating electrode insertion depths, which could serve as a radiation-free and cost-effective alternative to radiography. The secondary objective is to evaluate the reliability of the estimation method in the postoperative follow-up over several months.DesignThe ground truth insertion depths were measured from postoperative computed tomography scans obtained from the records of 56 cases with an identical lateral wall electrode array. For each of these cases, impedance telemetry records were retrieved starting from the day of implantation up to a maximum observation period of 60 mo. Based on these recordings, the linear and angular electrode insertion depths were estimated using a phenomenological model. The estimates obtained were compared with the ground truth values to calculate the accuracy of the model.ResultsAnalysis of the long-term recordings using a linear mixed-effects model showed that postoperative tissue resistances remained stable throughout the follow-up period, except for the two most basal electrodes, which increased significantly over time (electrode 11: ~10 Ω/year, electrode 12: ~30 Ω/year). Inferred phenomenological models from early and late impedance telemetry recordings were not different. The insertion depth of all electrodes was estimated with an absolute error of 0.9 mm ± 0.6 mm or 22° ± 18° angle (mean ± SD).ConclusionsInsertion depth estimations of the model were reliable over time when comparing two postoperative computed tomography scans of the same ear. Our results confirm that the impedance-based position estimation method can be applied to postoperative impedance telemetry recordings. Future work needs to address extracochlear electrode detection to increase the performance of the method.

Dataset Information

Online supervised attention-based recurrent depth estimation from monocular video.

Publications

Online supervised attention-based recurrent depth estimation from monocular video.

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