Monocular Depth Estimation with Sharp Boundary

Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious problem.Researchers find that the blurry boundary is mainly caused by two factors.First,the low-level features,containing boundary and structure information,may be lost in deep networks during the convolution process.Second,themodel ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area,during the backpropagation.Focusing on the factors mentioned above.Two countermeasures are proposed to mitigate the boundary blur problem.Firstly,we design a scene understanding module and scale transformmodule to build a lightweight fuse feature pyramid,which can deal with low-level feature loss effectively.Secondly,we propose a boundary-aware depth loss function to pay attention to the effects of the boundary’s depth value.Extensive experiments show that our method can predict the depth maps with clearer boundaries,and the performance of the depth accuracy based on NYU-Depth V2,SUN RGB-D,and iBims-1 are competitive.

SUFFICIENT AND NECESSARY CONDITIONS ON THE EXISTENCE AND ESTIMATES OF BOUNDARY BLOW-UP SOLUTIONS FOR SINGULAR p-LAPLACIAN EQUATIONS

Let?denote a smooth,bounded domain in R^(N)(N≥2).Suppose that g is a nondecreasing C^(1)positive function and assume that b(x)is continuous and nonnegative inΩ,and that it may be singular on■Ω.In this paper,we provide sufficient and necessary conditions on the existence of boundary blow-up solutions to the p-Laplacian problem△_(p)u=b(x)g(u)for x∈Ω,u(x)→+∞as dist(x,■Ω)→0.The estimates of such solutions are also investigated.Moreover,when b has strong singularity,the nonexistence of boundary blow-up(radial)solutions and infinitely many radial solutions are also considered.

A Simple Method for Source Depth Estimation with Multi-path Time Delay in Deep Ocean

A method of source depth estimation based on the multi-path time delay difference is proposed. When the minimum time arrivals in all receiver depths are snapped to a certain time on time delay-depth plane, time delay arrivals of surface-bottom reflection and bottom-surface reflection intersect at the source depth. Two hydrophones deployed vertically with a certain interval are required at least. If the receiver depths are known, the pair of time delays can be used to estimate the source depth. With the proposed method the source depth can be estimated successfully in a moderate range in the deep ocean without complicated matched-field calculations in the simulations and experiments.

Geophysical Study: Estimation of Deposit Depth Using Gravimetric Data and Euler Method (Jalalabad Iron Mine, Kerman Province of 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.

基于Shuffle-ZoeDepth单目深度估计的苗期玉米株高测量方法

株高是鉴别玉米种质性状及作物活力的重要表型指标,苗期玉米遗传特性表现明显,准确测量苗期玉米植株高度对玉米遗传特性鉴别与田间管理具有重要意义。针对传统植株高度获取方法依赖人工测量,费时费力且存在主观误差的问题,提出了一种融合混合注意力信息的改进ZoeDepth单目深度估计模型。改进后的模型将Shuffle Attention模块加入Decoder模块的4个阶段,使Decoder模块在对低分辨率特征图信息提取过程中能更关注特征图中的有效信息,提升了模型关键信息的提取能力,可生成更精确的深度图。为验证本研究方法的有效性,在NYU-V2深度数据集上进行了验证。结果表明,改进的Shuffle-ZoeDepth模型在NYU-V2深度数据集上绝对相对差、均方根误差、对数均方根误差为0.083、0.301 mm、0.036,不同阈值下准确率分别为93.9%、99.1%、99.8%,均优于ZoeDepth模型。同时,利用Shuffle-ZoeDepth单目深度估计模型结合玉米植株高度测量模型实现了苗期玉米植株高度的测量,采集不同距离下苗期玉米图像进行植株高度测量试验。当玉米高度在15~25 cm、25~35 cm、35~45 cm 3个区间时,平均测量绝对误差分别为1.41、2.21、2.08 cm,平均测量百分比误差分别为8.41%、7.54%、4.98%。试验结果表明该方法可仅使用单个RGB相机完成复杂室外环境下苗期玉米植株高度的精确测量。

Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting Data Augmentation Method

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.

Three-dimensional constrained gravity inversion of Moho depth and crustal structural characteristics at Mozambique continental margin

Mozambique's continental margin in East Africa was formed during the break-off stage of the east and west Gondwana lands. Studying the geological structure and division of continent-ocean boundary(COB) in Mozambique's continental margin is considered of great significance to rebuild Gondwana land and understand its movement mode. Along these lines, in this work, the initial Moho was fit using the known Moho depth from reflection seismic profiles, and a 3D multi-point constrained gravity inversion was carried out. Thus, highaccuracy Moho depth and crustal thickness in the study area were acquired. According to the crustal structure distribution based on the inversion results, the continental crust at the narrowest position of the Mozambique Channel was detected. According to the analysis of the crustal thickness, the Mozambique ridge is generally oceanic crust and the COB of the whole Mozambique continental margin is divided.

Depth estimation system suitable for hardware design

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.

GIS Based Methodology for Groundwater Flow Estimation across the Boundary of the Study Areain Groundwater Flow Modeling

Pali district, Rajasthan, India has been facing severe pollution of groundwater due to release of untreated industrial effluent of textile industries into the Bandi River flowing through the Pali city. A groundwater flow and transport modeling exercise has been undertaken by MNIT, Jaipur, India to understand the groundwater flow regime and to study the different scenarios. In the modeling exercise partially penetrating ephemeral rivers have been taken as part of model boundaries wherever more appropriate boundaries were not available in the near vicinity. These boundaries have been considered as constant flow boundaries. Aim of this paper is to present a methodology to calculate the average flux through such boundaries from readily available data such as bore logs and groundwater levels. The study area boundary was divided in to several cross sections and average values of groundwater flow gradients normal to the boundary were calculated for different monsoon and non monsoon seasons for different years. The entire boundary was then regrouped into 8 boundary segments on the basis of average values of gradients for individual line segments and mean gradient values for these line segments were calculated. Values of ground level, bottom elevations of hydros-tratigraphic layers and average water depth were extracted for a number of points on these line segments from the respective layers and these values were used to calculate equivalent horizontal hydraulic conductivity of the multi-layered aquifer system at every point. The Darcy’s law was then used to calculate inflow/outflow per m length of the boundary at each point. The methodology presented here is simple and is based on the assumption that the groundwater level gradients do not change significantly for different seasons and amongst different years which has been validated in the present groundwater modeling study. The paper demonstrates a GIS based methodology to work out inflow/outflow across boundary of a study area in the cases where no flow boundaries in the vicinity of the study area cannot be identified.

Boosting Unsupervised Monocular Depth Estimation with Auxiliary Semantic Information

Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer depth.We boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary task.To address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training,we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset,which not only provides a new way to conduct multi-task training,but also helps us to evaluate results compared with those of other algorithms.In addition,in order to comprehensively use the extracted features of the two tasks in the early perception stage,we use a strategy of sharing weights in the network to fuse cross-domain features,and introduce a novel multi-task loss function to further smooth the depth values.Extensive experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task,as well improved semantic segmentation.

Depth Estimation from a Single Image Based on Cauchy Distribution Model

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.

Real-Time Traffic State and Boundary Flux Estimation with Distributed Speed Detecting Networks

The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.

RADepthNet:Reflectance-aware monocular depth estimation

Background Monocular depth estimation aims to predict a dense depth map from a single RGB image,and has important applications in 3D reconstruction,automatic driving,and augmented reality.However,existing methods directly feed the original RGB image into the model to extract depth features without avoiding the interference of depth-irrelevant information on depth-estimation accuracy,which leads to inferior performance.Methods To remove the influence of depth-irrelevant information and improve the depth-prediction accuracy,we propose RADepthNet,a novel reflectance-guided network that fuses boundary features.Specifically,our method predicts depth maps using the following three steps:(1)Intrinsic Image Decomposition.We propose a reflectance extraction module consisting of an encoder-decoder structure to extract the depth-related reflectance.Through an ablation study,we demonstrate that the module can reduce the influence of illumination on depth estimation.(2)Boundary Detection.A boundary extraction module,consisting of an encoder,refinement block,and upsample block,was proposed to better predict the depth at object boundaries utilizing gradient constraints.(3)Depth Prediction Module.We use an encoder different from(2)to obtain depth features from the reflectance map and fuse boundary features to predict depth.In addition,we proposed FIFADataset,a depth-estimation dataset applied in soccer scenarios.Results Extensive experiments on a public dataset and our proposed FIFADataset show that our method achieves state-of-the-art performance.

Estimation of Parameters of Boundary Value Problems for Linear Ordinary Differential Equations with Uncertain Data

In this paper we construct optimal, in certain sense, estimates of values of linear functionals on solutions to two-point boundary value problems (BVPs) for systems of linear first-order ordinary differential equations from observations which are linear transformations of the same solutions perturbed by additive random noises. It is assumed here that right-hand sides of equations and boundary data as well as statistical characteristics of random noises in observations are not known and belong to certain given sets in corresponding functional spaces. This leads to the necessity of introducing minimax statement of an estimation problem when optimal estimates are defined as linear, with respect to observations, estimates for which the maximum of mean square error of estimation taken over the above-mentioned sets attains minimal value. Such estimates are called minimax mean square or guaranteed estimates. We establish that the minimax mean square estimates are expressed via solutions of some systems of differential equations of special type and determine estimation errors.

基于Boosting-Monodepth的管道病害深度估计与三维重建

城市地下管道是城市的血脉经络,但随着排水管道的大量投入运营和使用年限增加,引发了一系列的管道病害安全隐患,如管道整体结构变形、内表面破裂和管中异物插入等问题,传统的病害图像视频采集、检测和后期病害分类甄选都是从二维视角出发,欠缺对三维空间信息(深度)的考虑。针对上述3种病害从生成深度图、由二维深度图重建三维管道病害这两方面进行研究,提出了一种基于boosting-monodepth的双重深度估计方法以提升深度图效果,最终生成画面连续一致、轮廓清晰的深度图。性能评估方面采用Abs-Rel、RMSE、SqRel、ORD和D3R等通用指标,与传统算法对比,结果显示boosting-monodepth的RMSE值降低了30%,精确度指标δ<1.25时,模型深度信息预测精确度提高了18%,此后以得到的深度图为基础重建管道病害三维点云,并在CloudCompare软件上三维可视化,最后采用随机采样一致算法测算病害深度并和实测数据对比证明其有效性和准确性。

On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation

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://github.com/sunnyHelen/RCVC-depth.

An Estimation Method of Stress in Soft Rock Based on In-situ Measured Stress in Hard Rock

The law of variation of deep rock stress in gravitational and tectonic stress fields is analyzed based on the Hoek-Brown strength criterion. In the gravitational stress field,the rocks in the shallow area are in an elastic state and the deep,relatively soft rock may be in a plastic state. However,in the tectonic stress field,the relatively soft rock in the shallow area is in a plastic state and the deep rock in an elastic state. A method is proposed to estimate stress values in coal and soft rock based on in-situ measurements of hard rock. Our estimation method relates to the type of stress field and stress state. The equations of rock stress in various stress states are presented for the elastic,plastic and critical states. The critical state is a special stress state,which indicates the conversion of the elastic to the plastic state in the gravitational stress field and the conversion of the plastic to the elastic state in the tectonic stress field. Two cases stud-ies show that the estimation method is feasible.

A COUPLING METHOD OF DIFFERENCE WITH HIGH ORDER ACCURACY AND BOUNDARY INTEGRAL EQUATION FOR EVOLUTIONARY EQUATION AND ITS ERROR ESTIMATES

In the present paper, a new numerical method for solving initial-boundary value problems of evolutionary equations is proposed and studied, combining difference method with high accuracy with boundary integral equation method. The numerical approximate schemes for both problems on a bounded or unbounded domain in R3 are proposed and their prior error estimates are obtained.

Depth Estimates of Buried Utility Systems Using the GPR Method: Studies at the IAG/USP Geophysics Test Site

Identifying underground utilities and predicting their depth are fundamental when it comes to civil engineering excavations, for example, to install or repair water, sewer, gas, electric systems and others. The accidental rupture of these systems can lead to unplanned repair costs, delays in completing the service, and risk injury or death of workers. One way to detect underground utilities is using the GPR-Ground Penetrating Radar geophysical method. To estimate depth, the travel time (two-way travel time) information provided by a radargram is used in conjunction with ground wave velocity, which depends on the dielectric constant of materials, where it is usually assumed to be constant for the area under investigation. This procedure provides satisfactory results in most cases. However, wrong depth estimates can result in damage to public utilities, rupturing pipes, cutting lines and so on. These cases occur mainly in areas that have a marked variation of water content and/or soil lithology, thus greater care is required to determine the depth of the targets. The present work demonstrates how the interval velocity of Dix (1955) can be applied in radargram to estimate the depth of underground utilities compared to the conventional technique of constant velocity applied to the same data set. To accomplish this, synthetic and real GPR data were used to verify the applicability of the interval velocity technique and to determine the accuracy of the depth estimates obtained. The studies were carried out at the IAG/USP test site, a controlled environment, where metallic drums are buried in known positions and depths allowing the comparison of real to estimated depths. Numerical studies were also carried out aiming to simulate the real environment with variation of dielectric constant in depth and to validate the results with real data. The results showed that the depths of the targets were estimated more accurately by means of the interval velocity technique in contrast to the constant velocity technique, minimizing the risks of accidents during excavation.

Light Field Flow Estimation Based on Occlusion Detection

Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they can well solve many technical problems that cannot be done by conventional cameras. An important feature of light field cameras is that a microlens array is inserted between the sensor and main lens, through which a series of sub-aperture images of different perspectives are formed. Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property. This algorithm consists of three steps: 1) Computing the dense optical flow field among a group of sub-aperture images;2) Obtaining a robust depth-estimation by initializing the light-filed optical flow using the linear regression approach and detecting occluded areas using the consistency;3) Computing an improved light-field depth map by using the edge-preserving algorithm to realize interpolation optimization. The reliability and high accuracy of the proposed approach is validated by experimental results.