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On Robust Cross-view Consistency in Self-supervised Monocular depth estimation

Machine Intelligence Research 2024年第3期21卷 495-513页

作者： Haimei Zhao Jing Zhang Zhuo Chen Bo Yuan Dacheng Tao School of Computer Science University of SydneySydney2008Australia Shenzhen International Graduate School Tsinghua UniversityShenzhen518055China School of Information Technology&Electrical Engineering University of QueenslandBrisbane4072Australia

Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://***/sunnyHelen/RCVC-depth.

关键词： 3D vision depth estimation cross-view consistency self-supervised learning monocular perception.

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depth estimation Based on Monocular Camera Sensors in Autonomous Vehicles: A Self‑supervised Learning Approach

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Automotive Innovation 2023年第2期6卷 268-280页

作者： Guofa Li Xingyu Chi Xingda Qu College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China Institute of Human Factors and Ergonomics College of Mechatronics and Control EngineeringShenzhen University3688 Nanhai AvenueShenzhen 518060China

Estimating depth from images captured by camera sensors is crucial for the advancement of autonomous driving technologies and has gained significant attention in recent years.However,most previous methods rely on stacked pooling or stride convolution to extract high-level features,which can limit network performance and lead to information redundancy.This paper proposes an improved bidirectional feature pyramid module(BiFPN)and a channel attention module(Seblock:squeeze and excitation)to address these issues in existing methods based on monocular camera sensor.The Seblock redistributes channel feature weights to enhance useful information,while the improved BiFPN facilitates efficient fusion of multi-scale features.The proposed method is in an end-to-end solution without any additional post-processing,resulting in efficient depth estimation.Experiment results show that the proposed method is competitive with state-of-the-art algorithms and preserves fine-grained texture of scene depth.

关键词： Autonomous vehicle Camera sensor Deep learning depth estimation Self-supervised

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Semisupervised learning-based depth estimation with semantic inference guidance

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Science China(Technological Sciences) 2022年第5期65卷 1098-1106页

作者： ZHANG Yan FAN XiaoPeng ZHAO DeBin Department of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given scene.As it is an ill-posed problem,to fit the projection function from the given scene to the 3D structure,traditional methods generally require mass amounts of annotated data.Such pixel-level annotation is quite labor consuming,especially when addressing reflective surfaces such as mirrors or water.The widespread application of deep learning further intensifies the demand for large amounts of annotated data.Therefore,it is urgent and necessary to propose a framework that is able to reduce the requirement on the amount of data.In this paper,we propose a novel semisupervised learning framework to infer the 3D structure from the given scene.First,semantic information is employed to make the depth inference more accurate.Second,we make both the depth estimation and semantic segmentation coarse-to-fine frameworks;thus,the depth estimation can be gradually guided by semantic segmentation.We compare our model with state-of-the-art methods.The experimental results demonstrate that our method is better than many supervised learning-based methods,which proves the effectiveness of the proposed method.

关键词： depth estimation semisupervised learning semantic information neural networks

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Perpendicular-Cutdepth:Perpendicular Direction depth Cutting Data Augmentation Method

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Computers, Materials & Continua 2024年第4期79卷 927-941页

作者： Le Zou Linsong Hu Yifan Wang Zhize Wu Xiaofeng Wang Anhui Provincial Engineering Laboratory of Big Data Technology Application forUrban Infrastructure School ofArtificial Intelligence and Big DataHefei UniversityHefei230601China Institute of Applied Optimization School of Artificial Intelligence and Big DataHefei UniversityHefei230601China

depth estimation is an important task in computer vision.Collecting data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth information.Therefore,data augmentation is crucial for this task.Existing data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge features.In this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth method.This method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge features.To validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation algorithms.Additionally,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed algorithm.Experimental results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data augmentationmethods.On the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to 0.351.On the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

关键词： Perpendicular depth estimation data augmentation

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depth estimation system suitable for hardware design

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Journal of Shanghai University(English Edition) 2011年第4期15卷 325-330页

作者：李贺建左一帆杨高波安平王建伟滕国伟 Key Laboratory of Advanced (Shanghai University) Display and System Application Ministry of EducationShanghai 200072P.R.China College of Computer and Communication Hunan UniversityChangsha 410082P.R.China School of Communication and Information Engineering Shanghai UniversityShanghai 200072P.R.China

depth estimation is an active research area with the developing of stereo vision in recent years. It is one of the key technologies to resolve the large data of stereo vision communication. Now depth estimation still has some problems, such as occlusion, fuzzy edge, real-time processing, etc. Many algorithms have been proposed base on software, however the performance of the computer configurations limits the software processing speed. The other resolution is hardware design and the great developments of the digital signal processor （DSP）, and application specific integrated circuit （ASIC） and field programmable gate array （FPGA） provide the opportunity of flexible applications. In this work, by analyzing the procedures of depth estimation, the proper algorithms which can be used in hardware design to execute real-time depth estimation are proposed. The different methods of calibration, matching and post-processing are analyzed based on the hardware design requirements. At last some tests for the algorithm have been analyzed. The results show that the algorithms proposed for hardware design can provide credited depth map for further view synthesis and are suitable for hardware design.

关键词： 3-D TV （3DTV） depth estimation hardware design rank transform census transform

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estimation of depth to Salt Domes from Normalized Full Gradient of Gravity Anomaly and Examples from the USA and Denmark

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Journal of Earth Science 2009年第6期20卷 1012-1016页

作者： Hamid Aghajani Ali Moradzadeh 曾华霖 Faculty of Mining Petroleum and GeophysicsShahrood University of Technology School of Geophysics and Information Technology China University of GeosciencesBeijing

We present an estimation of depth of anomalous bodies using normalized full gradient （NFG） of gravity anomaly. Maxima in the NFG map can locate the bodies and indicate their depth. Model calculation using a sphere and application of the NFG method to gravity anomalies over salt domes in the USA and Denmark shows effectiveness of the method. However, the accuracy of depth estimation strongly depends on the number of term N in the Fourier series used to calculate the NFG. An optimum N for the calculation can be given from a test.

关键词： depth estimation anomalous body normalized full gradient gravity anomaly Fourier series.

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Semi-Global depth estimation Algorithm for Mobile 3-D Video Applications

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Tsinghua Science and Technology 2012年第2期17卷 128-135页

作者： Qiong Liu Hongjiang Xiao Department of Automation Tsinghua UniversityBeijing 100084China Department of Electronics and Information Engineering Huazhong University of Science and TechnologyWuhan 430074China Academy of Broadcasting Planning SARFT Beijing 100866China

Three-dimensional （3-D） video applications, such as 3-D cinema, 3DTV, ana Free Viewpomt Video （FVV） are attracting more attention both from the industry and in the literature. High accuracy of depth video is a fundamental prerequisite for most 3-D applications. However, accurate depth requires computationally intensive global optimization. This high computational complexity is one of the bottlenecks to applying depth generation to 3-D applications, especially for mobile networks since mobile terminals usually have limited computing ability. This paper presents a semi-global depth estimation algorithm based on temporal consis- tency, where the depth propagation is used to generate initial depth values for the computationally intensive global optimization. The accuracy of initial depth is improved by detecting and eliminating the depth propagation outliers before the global optimization. Integrating the initial values without outliers into the global optimization reduces the computational complexity while maintaining the depth accuracy. Tests demonstrate that the algorithm reduces the total computational time by 54%-65% while the quality of the virtual views is essentially equivalent to the benchmark.

关键词： 3-D video depth estimation temporal propagation graph cuts

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depth estimation from a Single Image Based on Cauchy Distribution Model

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Journal of Computer and Communications 2021年第3期9卷 133-142页

作者： Ying Ming College of Politics National Defense University of PLA Shanghai China

Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those methods are suffered from some noises, and difficult to get a high quality of depth recovery. We presented a simple yet effective approach to estimate exactly the amount of spatially varying defocus blur at edges, based on a Cauchy distribution model for the PSF. The raw image was re-blurred twice using two known Cauchy distribution kernels, and the defocus blur amount at edges could be derived from the gradient ratio between the two re-blurred images. By propagating the blur amount at edge locations to the entire image using the matting interpolation, a full depth map was then recovered. Experimental results on several real images demonstrated both feasibility and effectiveness of our method, being a non-Gaussian model for DSF, in providing a better estimation of the defocus map from a single un-calibrated defocused image. These results also showed that our method was robust to image noises, inaccurate edge location and interferences of neighboring edges. It could generate more accurate scene depth maps than the most of existing methods using a Gaussian based DSF model.

关键词： depth estimation depth From Defocus Defocus Blur Gaussian Gradient Cauchy Distribution Point Spread Function (PSF)

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Geophysical Study: estimation of Deposit depth Using Gravimetric Data and Euler Method (Jalalabad Iron Mine, Kerman Province of IRAN)

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Open Journal of Geology 2021年第8期11卷 340-355页

作者： Adel Shirazy Aref Shirazi Hamed Nazerian Keyvan Khayer Ardeshir Hezarkhani Department of Mining Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Civil Engineering and Architecture University of Catania Catania Italy Faculty of Mining Petroleum & Geophysics Engineering Shahrood University of Technology Shahrood Iran Amirkabir University of Technology Tehran Iran

Mineral exploration is done by different methods. Geophysical and geochemical studies are two powerful tools in this field. In integrated studies, the results of each study are used to determine the location of the drilling boreholes. The purpose of this study is to use field geophysics to calculate the depth of mineral reserve. The study area is located 38 km from Zarand city called Jalalabad iron mine. In this study, gravimetric data were measured and mineral depth was calculated using the Euler method. 1314 readings have been performed in this area. The rocks of the region include volcanic and sedimentary. The source of the mineralization in the area is hydrothermal processes. After gravity measuring in the region, the data were corrected, then various methods such as anomalous map remaining in levels one and two, upward expansion, first and second-degree vertical derivatives, analytical method, and analytical signal were drawn, and finally, the depth of the deposit was estimated by Euler method. As a result, the depth of the mineral deposit was calculated to be between 20 and 30 meters on average.

关键词： Geophysical Study depth estimation Gravimetric Data Euler Method Jalalabad Iron Mine

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Dense depth estimation with Absolute Scale

Dense Depth Estimation with Absolute Scale

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第37届中国控制会议

作者： Xing Jin Zhiwen Yao Jingjing Zhang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Considering the difficulties in estimating depth from single image, in this paper, we propose a method to obtain the absolute scale depth map by combining the convolution neural network and depth filter. We compute relative transformation between consecutive frames by direct tracking features, which are extracted from RGB images and whose depthes are predicted by deep network, and then optimize relative motion by searching for a better feature alignment in epipolar line, and finally update every pixel depth of the reference frame by depth filter. We evaluate the proposed method on the open dataset comparison against the state of the art in depth estimation to evaluate our method.

关键词： depth estimation Pose estimation depth Filter

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