Single Image Depth Prediction with Wavelet Decomposition

Ramamonjisoa, Michaël; Firman, Michael; Watson, Jamie; Lepetit, Vincent; Turmukhambetov, Daniyar

doi:10.48550/arxiv.2106.02022

Cited by 1 publication

(2 citation statements)

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“…Secondly, we compared the proposed model with four state-of-the-art methods [ 12 , 33 , 34 , 35 ]. We show evaluation measures on the NYU Depth-v2 dataset with the four tested approaches and the proposed model in Table 3 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The proposed model outperformed the four methods in terms of the three measures (

of a threshold of

and

, and

and the

error).

of a threshold of

with our model was improved by

compared to [ 34 ], the second-best method. In turn, with

, [ 12 ], our method achieved an improvement of

compared to the other three methods.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Deep Monocular Depth Estimation Based on Content and Contextual Features

Abdulwahab

Rashwan

Sharaf

et al. 2023

Sensors

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Recently, significant progress has been achieved in developing deep learning-based approaches for estimating depth maps from monocular images. However, many existing methods rely on content and structure information extracted from RGB photographs, which often results in inaccurate depth estimation, particularly for regions with low texture or occlusions. To overcome these limitations, we propose a novel method that exploits contextual semantic information to predict precise depth maps from monocular images. Our approach leverages a deep autoencoder network incorporating high-quality semantic features from the state-of-the-art HRNet-v2 semantic segmentation model. By feeding the autoencoder network with these features, our method can effectively preserve the discontinuities of the depth images and enhance monocular depth estimation. Specifically, we exploit the semantic features related to the localization and boundaries of the objects in the image to improve the accuracy and robustness of the depth estimation. To validate the effectiveness of our approach, we tested our model on two publicly available datasets, NYU Depth v2 and SUN RGB-D. Our method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy of 85%, while minimizing the error Rel by 0.12, RMS by 0.523, and log10 by 0.0527. Our approach also demonstrated exceptional performance in preserving object boundaries and faithfully detecting small object structures in the scene.

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