Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

Li, Qing; Zhu, Jiasong; Liu, Jun; Cao, Rui; Li, Qingquan; Jia, Sen; Qiu, Guoping

doi:10.48550/arxiv.2011.04123

Cited by 3 publications

(3 citation statements)

References 152 publications

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“…Recently, numerous monocular deep learning algorithms have been proposed that, when combined with AI-capable chips, can achieve a balance between computational power and cost (latency <100 ms, chip cost < USD 50, chip power consumption <3 W). Among these, convolutional neural network (CNNs) models, widely applied in the field of computer vision, have begun to be utilized in monocular height estimation [11]. CNNs excel in reconstructing details like edges and offer controllable computational complexity.…”

Section: Introductionmentioning

confidence: 99%

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

Chen,

Zhang,

et al. 2024

Remote Sensing

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Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, with monocular height estimation offering wide-ranging data sources and convenient deployment. This paper addresses the existing challenges in monocular height estimation methods, namely the difficulty in simultaneously achieving high-quality instance-level height and edge reconstruction, along with high computational complexity. This paper presents a comprehensive solution for monocular height estimation in remote sensing, termed HeightFormer, combining multilevel interactions and image-adaptive classification–regression. It features the Multilevel Interaction Backbone (MIB) and Image-adaptive Classification–regression Height Generator (ICG). MIB supplements the fixed sample grid in the CNN of the conventional backbone network with tokens of different interaction ranges. It is complemented by a pixel-, patch-, and feature map-level hierarchical interaction mechanism, designed to relay spatial geometry information across different scales and introducing a global receptive field to enhance the quality of instance-level height estimation. The ICG dynamically generates height partition for each image and reframes the traditional regression task, using a refinement from coarse to fine classification–regression that significantly mitigates the innate ill-posedness issue and drastically improves edge sharpness. Finally, the study conducts experimental validations on the Vaihingen and Potsdam datasets, with results demonstrating that our proposed method surpasses existing techniques.

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Section: Introductionmentioning

confidence: 99%

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

Chen,

Zhang,

et al. 2024

Remote Sensing

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“…Computer Vision has evolved dramatically with the introduction of Deep Learning (DL) models [ 1 ] as the accuracy and the effectiveness of deep convolutional neural networks [ 2 ] became impressive. DL has been applied with great success to nearly all CV tasks e.g., classification [ 2 ], semantic segmentation [ 3 ], object detection [ 4 ], pose estimation [ 5 ], quality assessment [ 6 ] and depth prediction [ 7 ]. The application of CV in Medical Image Analysis [ 8 ] is mostly referred to segmentation and classification problems.…”

Section: Introductionmentioning

confidence: 99%

How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA)

2022

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In the past years, deep neural networks (DNNs) have become popular in many disciplines such as computer vision (CV). One of the most important challenges in the CV area is Medical Image Analysis (MIA). However, adversarial attacks (AdAs) have proven to be an important threat to vision systems by significantly reducing the performance of the models. This paper proposes a new black-box adversarial attack, which is based οn orthogonal image moments named Mb-AdA. Additionally, a corresponding defensive method of adversarial training using Mb-AdA adversarial examples is also investigated, with encouraging results. The proposed attack was applied in classification and segmentation tasks with six state-of-the-art Deep Learning (DL) models in X-ray, histopathology and nuclei cell images. The main advantage of Mb-AdA is that it does not destroy the structure of images like other attacks, as instead of adding noise it removes specific image information, which is critical for medical models’ decisions. The proposed attack is more effective than compared ones and achieved degradation up to 65% and 18% in terms of accuracy and IoU for classification and segmentation tasks, respectively, by also presenting relatively high SSIM. At the same time, it was proved that Mb-AdA adversarial examples can enhance the robustness of the model.

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“…However, accurate dense depth perception with LiDAR is normally expensive, thus limit it for mass production. The depth prediction from monocular images [2,3] is cost-effective and attracting more and more attention from both research and industry communities.…”

Section: Introductionmentioning

confidence: 99%

Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

Feng¹,

Jing²,

Yin³

et al. 2021

Preprint

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Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots. In this paper, we propose a novel two-stage network to advance the self-supervised monocular dense depth learning by leveraging low-cost sparse (e.g. 4-beam) Li-DAR. Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to predict initial depth maps. Then, an efficient feed-forward refine network is further designed to correct the errors in these initial depth maps in pseudo-3D space with real-time performance. Extensive experiments show that our proposed model significantly outperforms all the stateof-the-art self-supervised methods, as well as the sparse-LiDAR-based methods on both self-supervised monocular depth prediction and completion tasks. With the accurate dense depth prediction, our model outperforms the state-of-the-art sparse-LiDAR-based method (Pseudo-LiDAR++ [1]) by more than 68% for the downstream task monocular 3D object detection on the KITTI Leaderboard.

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Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

Cited by 3 publications

References 152 publications

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA)

Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

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