Practical Depth Estimation with Image Segmentation and Serial U-Nets

Cantrell, Kyle; Miller, Craig; Morato, Carlos W.

doi:10.5220/0009781800002550

Cited by 4 publications

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“…The three maps in parallel are concatenated along the channel axis to form a tensor of the shape (480, 640, 5). Further, it is fed into a 2D-UNet model with a ResNet-50 backbone [18] and trained end-to-end. The 3DBGES-UNet model is trained for 150 epochs on NYU-Depth v2 data considering 20K samples for training and 694 samples for testing.…”

Section: Proposed 3dbges-unet For Monocular Depth Estimationmentioning

confidence: 99%

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A Novel 3D-Unet Deep Learning Framework Based on High-Dimensional Bilateral Grid for Edge Consistent Single Image Depth Estimation

Sharma

Tushar

et al. 2020

2020 International Conference on 3D Immersion (IC3D)

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The task of predicting smooth and edge-consistent depth maps is notoriously difficult for single image depth estimation. This paper proposes a novel Bilateral Grid based 3D convolutional neural network, dubbed as 3DBG-UNet, that parameterizes high dimensional feature space by encoding compact 3D bilateral grids with UNets and infers sharp geometric layout of the scene. Further, another novel 3DBGES-UNet model is introduced that integrate 3DBG-UNet for inferring an accurate depth map given a single color view. The 3DBGES-UNet concatenates 3DBG-UNet geometry map with the inception network edge accentuation map and a spatial object's boundary map obtained by leveraging semantic segmentation and train the UNet model with ResNet backbone. Both models are designed with a particular attention to explicitly account for edges or minute details. Preserving sharp discontinuities at depth edges is critical for many applications such as realistic integration of virtual objects in AR video or occlusion-aware view synthesis for 3D display applications.The proposed depth prediction network achieves state-of-theart performance in both qualitative and quantitative evaluations on the challenging NYUv2-Depth data. The code and corresponding pre-trained weights will be made publicly available.

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Section: Proposed 3dbges-unet For Monocular Depth Estimationmentioning

confidence: 99%

“…We quantitatively compare our 3DBGES-UNet model with CNN algorithms Cantrell et al [18] (Serial U-Net), Ramamonjisoa et al [8] (SharpNet), Eigen et al [26] (MSDN) in Table 1. We evaluate these methods using most common error metrics from prior works [18,8,26]. The error metrics are defined as:…”

Section: Evaluation and Comparative Analysismentioning

confidence: 99%

A Novel 3D-Unet Deep Learning Framework Based on High-Dimensional Bilateral Grid for Edge Consistent Single Image Depth Estimation

Sharma

Tushar

et al. 2020

2020 International Conference on 3D Immersion (IC3D)

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“…Unlike the previously mentioned alternatives, utilizing cameras (mono or stereo) for depth estimation offers a more affordable approach [4]. Although stereo cameras can provide the required depth information [5], they are frequently orders of magnitude more expensive than a monocular camera [6].…”

Section: Introductionmentioning

confidence: 99%

A Region-Based Approach to Monocular Mapless Navigation Using Deep Learning Techniques

Machkour,

Ortiz-Arroyo,

Durdevic

2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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In recent years, there have been significant advances in navigation methods for autonomous robotic systems, giving rise to a diverse range of navigation techniques. These techniques include GPS-based, SLAM-based, and monocular depth-based navigation. However, each of these approaches has its limitations. Typically, these techniques rely on either external sensors and positioning systems or require the creation of a local map prior to initiating navigation. This paper introduces a new approach for autonomous navigation of ground robots: mapless navigation using a pre-trained monocular depth network. This technique offers an efficient and cost-effective way of navigating without the need for a pre-existing map of the environment. To evaluate and compare the performance of our method, we conducted experiments using two different depth estimation models tested within the Gazebo simulation environment.

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“…Cantrell et al. [15] proposed to combine semantic segmentation and transfer learning technology to improve the accuracy of depth prediction. Hu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Watson et al [14] introduced depth hints to alleviate the problem of fuzzy re projection in depth prediction. Cantrell et al [15] proposed to combine semantic segmentation and transfer learning technology to improve the accuracy of depth prediction. Hu et al [16] proposed using multi-scale feature fusion module to obtain higher spatial resolution depth map.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised monocular image depth prediction algorithm using Fourier domain analysis

Chen

Tang

2022

IET Signal Processing

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Aiming at the problems of high cost and low accuracy of scene details during the depth map generation in 3D reconstruction, we propose an unsupervised monocular image depth prediction algorithm based on Fourier domain analysis. Generally speaking, smallscale images can better display depth details, while large-scale images can more reliably display the depth distribution value of the entire image. In order to take advantage of these complementary properties, we crop the input image with different cropped image ratios to generate multiple disparity map candidates, and then use Fourier frequency domain analysis algorithms to fuse disparity mapping candidates into left and right disparity maps. At the same time, we propose a loss function based on MSSIM to compensate the difference between left and right views and realize unsupervised monocular image depth prediction model training. Experimental results show that our method has good performance on the KITTI dataset.

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Practical Depth Estimation with Image Segmentation and Serial U-Nets

Cited by 4 publications

References 0 publications

A Novel 3D-Unet Deep Learning Framework Based on High-Dimensional Bilateral Grid for Edge Consistent Single Image Depth Estimation

A Novel 3D-Unet Deep Learning Framework Based on High-Dimensional Bilateral Grid for Edge Consistent Single Image Depth Estimation

A Region-Based Approach to Monocular Mapless Navigation Using Deep Learning Techniques

An unsupervised monocular image depth prediction algorithm using Fourier domain analysis

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