Abstract
Depth estimation is a key problem in 3D computer vision and has a wide variety of applications. In this paper we explore whether deep learning network can predict depth map accurately by learning multi-scale spatio-temporal features from sequences and recasting the depth estimation from a regression task to an ordinal classification task. We design an encoder-decoder network with several multi-scale strategies to improve its performance and extract spatio-temporal features with ConvLSTM. The results of our experiments show that the proposed method has an improvement of almost 10% in error metrics and up to 2% in accuracy metrics. The results also tell us that extracting spatio-temporal features can dramatically improve the performance in depth estimation task. We consider to extend this work to a self-supervised manner to get rid of the dependence on large-scale labeled data.
Highlights
We evaluated our approach by using the standard metrics proposed by [44]
We design a deep learning neural network to extract spatio-temporal features from sequences, motivated by [46], and predict the depth map from it by ordinal classification, inspired by [47]
The network encodes the input by ResNet Bottleneck and ConvLSTM, decodes and recovers the resolution of input images by Convolution and DeConvolution with skip-connection from encoder
Summary
Depth estimation [1,2,3,4,5,6,7] is a longstanding and fundamental task in 3D computer vision and enables a wide variety of applications, e.g., autonomous driving [8,9,10], Augmented Reality (AR) and Virtual Reality (VR) [11,12], Simultaneous Localization And Mapping (SLAM) [13,14,15,16,17,18], 2D-3D video conversion [19] and 3D scene understanding [20,21]. Most methods to estimate depth fall into three categories: Monocular Depth Estimation, Stereo Depth Estimation and Depth from Motion (Sequence). We find that most recently proposed methods focus on Monocular Depth Estimation, as it is more difficult to solve academically. Such methods ignore one of the most important features for determining depth in the human vision system, which is motion, and in most applications, the format of input is in sequence
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
