Enhancing Self-Supervised Monocular Depth Estimation with Traditional Visual Odometry

Andraghetti, Lorenzo; Myriokefalitakis, Panteleimon; Dovesi, Pier Luigi; Luque, Belen; Poggi, Matteo; Pieropan, Alessandro; Mattoccia, Stefano

doi:10.1109/3dv.2019.00054

Cited by 42 publications

(30 citation statements)

References 48 publications

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“…Supervised ACAN [46] Encoder-Decoder DenseDepth [47] Encoder-Decoder DORN [18] CNN VNL [48] Encoder-Decoder BTS [49] DeepV2D [50] Encoder-Decoder CNN LISM [51] Encoder-Decoder Self-supervised monoResMatch [38] CNN PackNet-SfM [52] CNN VOMonodepth [53] Auto-Decoder monodepth2 [42] CNN GASDA [54] CNN Semi-supervised…”

Section: Emdeom [32] Fcmentioning

confidence: 99%

Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Khan

Salahuddin

Javidnia

2020

Sensors

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Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.

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Section: Emdeom [32] Fcmentioning

confidence: 99%

Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Khan

Salahuddin

Javidnia

2020

Sensors

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“…Among these, ref. [12] is the first notable attempt leveraging stereo pairs, eventually improved exploiting traditional stereo algorithm [13,14], visual odometry supervision [15,16] or 3D movies [17]. On the other hand, methods leveraging monocular videos do not even require a stereo camera at training time, at the cost of learning depth estimation up to a scale factor.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Aleotti

Zaccaroni

Bartolomei

et al. 2020

Sensors

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Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.

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“…Bian et al [4] improve the scale consistency via using depth clues. To leverage the privilege of traditional 3D geometry, Andraghetti et al [2] enhance the self-supervised framework by traditional visual odometry. There also exist works [48,63,69] that constrain the network via introducing extra information (optical flows.…”

Section: Self-supervised Depth Estimationmentioning

confidence: 99%

Enhancing Self-supervised Monocular Depth Estimation via Incorporating Robust Constraints

Zhu

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Self-supervised depth estimation has shown great prospects in inferring 3D structures using purely unannotated images. However, its performance usually drops when trained on the images with changing brightness and moving objects. In this paper, we address this issue by enhancing the robustness of the self-supervised paradigm using a set of image-based and geometry-based constraints. Our contributions are threefold, 1) we propose a gradient-based robust photometric loss which restrains the false supervisory signals caused by brightness changes, 2) we propose to filter out the unreliable areas that violate the rigid assumption by a novel combined selective mask, which is computed on the forward pass of the network by leveraging the inter-loss consistency and the lossgradient consistency, and 3) we constrain the motion estimation network to generate across-frame consistent motions via proposing a triplet-based cycle consistency constraint. Extensive experiments conducted on KITTI, Cityscape and Make3D datasets demonstrate the superiority of our method, that the proposed method can effectively handle complex scenes with changing brightness and object motions. Both qualitative and quantitative results show that the proposed method outperforms the state-of-the-art methods. CCS CONCEPTS • Computing methodologies → 3D imaging; Motion capture.

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Enhancing Self-Supervised Monocular Depth Estimation with Traditional Visual Odometry

Cited by 42 publications

References 48 publications

Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Enhancing Self-supervised Monocular Depth Estimation via Incorporating Robust Constraints

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