Cost Volume Research Articles

Uanet: uncertainty-aware cost volume aggregation-based multi-view stereo for 3D reconstruction

Uanet: uncertainty-aware cost volume aggregation-based multi-view stereo for 3D reconstruction

Analisis Dampak Covid-19 pada Volume Transaksi Saham dan Harga Saham: Studi Kasus Pada Kelompok Perusahaan Transportasi yang Terdaftar di BEI Tahun 2020

The reason for this study was to decide the effect of Coronavirus on stock exchange volume and stock costs. The object of this exploration is a gathering of transportation organizations recorded on the Indonesia Stock Exchange in 2020. The populace in this study is 42 transportation organizations. What's more, the example utilized is upwards of 20 organizations. This study utilizes optional information. The information examination strategy utilized in this exploration is elucidating subjective information examination method. The consequences of this study demonstrate that the volume of stock exchanges and costs impacted by the COVID-19 pandemic diminished, upwards of three organizations, including area, air and ocean transportation, which encountered a decay. What's more, that accomplished an increment upwards of 4 organizations, remembering ocean transportation organizations and land transportation organizations for delivery administrations.

The Adversarial Robust and Generalizable Stereo Matching for Infrared Binocular Based on Deep Learning

Despite the considerable success of deep learning methods in stereo matching for binocular images, the generalizability and robustness of these algorithms, particularly under challenging conditions such as occlusions or degraded infrared textures, remain uncertain. This paper presents a novel deep-learning-based depth optimization method that obviates the need for large infrared image datasets and adapts seamlessly to any specific infrared camera. Moreover, this adaptability extends to standard binocular images, allowing the method to work effectively on both infrared and visible light stereo images. We further investigate the role of infrared textures in a deep learning framework, demonstrating their continued utility for stereo matching even in complex lighting environments. To compute the matching cost volume, we apply the multi-scale census transform to the input stereo images. A stacked sand leak subnetwork is subsequently employed to address the matching task. Our approach substantially improves adversarial robustness while maintaining accuracy on comparison with state-of-the-art methods which decrease nearly a half in EPE for quantitative results on widely used autonomous driving datasets. Furthermore, the proposed method exhibits superior generalization capabilities, transitioning from simulated datasets to real-world datasets without the need for fine-tuning.

AP-Net: Attention-fused volume and progressive aggregation for accurate stereo matching

Stereo matching plays an important role in advancing substantial applications such as 3D reconstruction, autonomous driving, depth sensing, and has recently made tremendous progress. It still poses a significant challenge, however, in the extraction of disparities from a pair of rectified images to achieve accurate stereo matching. In this paper, we propose an attention-fused cost volume construction and progressive cost aggregation network, namely AP-Net, to elevate the accuracy of stereo matching. Specifically, we introduce attention fusion to learn feature similarity between corresponding points on the left and right images while capturing long-dependent contextual information to construct attention-fused cost volumes. Moreover, we propose progressive cost aggregation, which consists of two stages to fully exploit information from different cost volumes. Furthermore, to make more accurate adjustments to the initial disparity, we implement an information entropy-guided disparity refinement module, which leverages information entropy as an explicit confidence measure of the initial disparity estimation to guide the refinement module in calculating the disparity residual more accurately and improving the overall accuracy of disparity estimation. Our implemented disparity refinement module can also be seamlessly embedded into a variety of stereo matching networks, significantly improving the model’s performance with only a small increase in computation and parameters. Experimental results across various stereo datasets confirm that our proposed AP-Net consistently delivers competitive performance. The code is available at https://github.com/zhuys-bupt/AP-Net.

Efficient Multi-View Stereo by Dynamic Cost Volume and Cross-Scale Propagation

Efficient Multi-View Stereo by Dynamic Cost Volume and Cross-Scale Propagation

Robust structured light with efficient redundant codes

Structured light (SL) systems acquire high-fidelity 3D geometry with active illumination projection. Conventional systems exhibit challenges when working in environments with strong ambient illumination. This paper studies a general-purposed solution to improve the robustness of SL by projecting a redundant number of patterns. Despite sacrificing the signal-noise-ratio at each frame, projected signals become more distinguishable from errors. Thus, the geometry can be recovered easily. We systematically analyze the redundant SL code design rules to achieve high accuracy with minimum redundancy. Based on the more reliable correspondence cost volume and the natural image prior, we integrate spatial context-aware disparity estimators into our system to further boost performance. We also demonstrate the application of such techniques in iterative error detection and refinement. We demonstrate significant performance improvements of efficient redundant code SL systems in both simulations and challenging real-world scenes.

EnhancedNet, an End-to-End Network for Dense Disparity Estimation and its Application to Aerial Images

Recent developments in deep learning technology have boosted the performance of dense stereo reconstruction. However, the state-of-the-art deep learning-based stereo matching methods are mainly trained using close-range synthetic images. Consequently, the application of these methods in aerial photogrammetry and remote sensing is currently far from straightforward. In this paper, we propose a new disparity estimation network for stereo matching and investigate its generalization abilities in regard to aerial images. First, we propose an end-to-end deep learning network for stereo matching, regularized by disparity gradients, which includes a residual cost volume and a reconstruction error volume in a refinement module, and multiple losses. In order to investigate the influence of the multiple losses, a comprehensive analysis is presented. Second, based on this network trained with synthetic close-range data, we propose a new pipeline for matching high-resolution aerial imagery. The experimental results show that the proposed network improves the disparity accuracy by up to 40% in terms of errors larger than 1 px compared to results when not including the refinement network, especially in areas containing detailed small objects. In addition, in qualitative and quantitative experiments, we are able to show that our model, pre-trained on a synthetic stereo dataset, achieves very competitive sub-pixel geometric accuracy on aerial images. These results confirm that the domain gap between synthetic close-range and real aerial images can be satisfactorily bridged using the proposed new deep learning method for dense image matching.

Cost Volume Profit Analysis as a Profit Planning Tool at Buket Husnul

UMKM Buket Husnul is a business in the creative industry that sells products in the form of flower bouquets with various models. In its profit planning Buket Husnul does not yet have a planning tool, therefore this study discusses related to profit planning at UMKM Buket Husnul which utilizes Cost-Volume-Profit analysis as a tool to plan its profits. Cost-Volume-Profit analysis is a very good profit planning tool to help business people make decisions in order to optimize their business profits. In this study, researchers used a type of research with descriptive qualitative methods, based on data sources from interviews and direct observation in the field. The research results of the cost volume profit analysis that have been carried out are for the contribution margin of Rp43,125,000.00 with a contribution ratio of 52%. In order to reach the break-even point or Break-Event- Point the business owner must be able to sell a bouquet of 1,100 units with an income of Rp. 8,269,231.00 per quarter so that the business does not get a loss, with the maximum Margin of Safety that can only decrease by Rp. 74,230,769.00 with a ratio of 89.98% and the results of Operating-Leverage of 1.12 times operating profit.

Stereo Matching Method with Cost Volume Collaborative Filtering

Aiming at the problem of matching ambiguity and low disparity accuracy at the object boundary in stereo matching, a novel stereo matching algorithm with cost volume collaborative filtering is proposed. Firstly, for each pixel, two support windows are built, namely a local cross- support window as well as a global support window for the whole image. Secondly, a new adaptive weighted guide filter with a cross-support window as a kernel window is derived, and it is used to locally filter the cost volume. In addition, a minimum spanning tree is constructed in the whole image window, and then the minimum spanning tree filter is used to globally filter the cost volume. The collaborative filtering of cost volume is realized by fusing the filtering results of the local filter and global filter, so that each pixel can not only receive the support of the neighboring pixels in the local adaptive window, but can also receive the effective support of other pixels in the whole image, thus effectively eliminating the matching ambiguity in different texture regions while maintaining the disparity edges. The experimental results show that the average matching error rate of our method on the Middlebury stereo images is 3.17%. Compared with the other state-of-the-art methods, our method has higher robustness and matching accuracy, the generated disparity maps are smoother, and the disparity edges are better preserved.

A vision measurement method for ship hull plates based on multi-view stereo and image segmentation

The background denoising of point clouds is challenging for the measurement of ship hull plates. To address this issue, a vision measurement method is proposed by combining an identity cost volume network (ICV-Net) and a segment anything model (SAM). By applying the ship hull plate masks segmented by SAM to the corresponding depth maps inferred by ICV-Net, the background noise can be directly removed at the depth map level. Additionally, an edge extraction optimization algorithm complementary to SAM is proposed to eliminate the thickness-related noise. Moreover, an efficient depth map refinement algorithm is proposed by imposing the smoothness prior of ship hull plate surfaces and multi-view photometric consistency. Sub-pixel accurate depth maps of ship hull plates can be obtained by the depth map refinement. Experimental results suggest that the proposed method can automatically, accurately and efficiently remove the background noise of ship hull plates, which is crucial toward practical application.

Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching

Perception of digitized depth is a prerequisite for enabling the intelligence of three-dimensional (3D) laparoscopic systems. In this context, stereo matching of laparoscopic stereoscopic images presents a promising solution. However, the current research in this field still faces challenges. First, the acquisition of accurate depth labels in a laparoscopic environment proves to be a difficult task. Second, errors in the correction of laparoscopic images are prevalent. Finally, laparoscopic image registration suffers from ill-posed regions such as specular highlights and textureless areas. In this paper, we make significant contributions by developing (1) a correction compensation module to overcome correction errors; (2) an adaptive cost aggregation module to improve prediction performance in ill-posed regions; (3) a novel self-supervised stereo matching framework based on these two modules. Specifically, our framework rectifies features and images based on learned pixel offsets, and performs differentiated aggregation on cost volumes based on their value. The experimental results demonstrate the effectiveness of the proposed modules. On the SCARED dataset, our model reduces the mean depth error by 12.6% compared to the baseline model and outperforms the state-of-the-art unsupervised methods and well-generalized models.

High-Performance Binocular Disparity Prediction Algorithm for Edge Computing.

End-to-end disparity estimation algorithms based on cost volume deployed in edge-end neural network accelerators have the problem of structural adaptation and need to ensure accuracy under the condition of adaptation operator. Therefore, this paper proposes a novel disparity calculation algorithm that uses low-rank approximation to approximately replace 3D convolution and transposed 3D convolution, WReLU to reduce data compression caused by the activation function, and unimodal cost volume filtering and a confidence estimation network to regularize cost volume. It alleviates the problem of disparity-matching cost distribution being far away from the true distribution and greatly reduces the computational complexity and number of parameters of the algorithm while improving accuracy. Experimental results show that compared with a typical disparity estimation network, the absolute error of the proposed algorithm is reduced by 38.3%, the three-pixel error is reduced to 1.41%, and the number of parameters is reduced by 67.3%. The calculation accuracy is better than that of other algorithms, it is easier to deploy, and it has strong structural adaptability and better practicability.

Bidirectional Hybrid LSTM Based Recurrent Neural Network for Multi-View Stereo.

Recently, deep learning based multi-view stereo (MVS) networks have demonstrated their excellent performance on various benchmarks. In this paper, we present an effective and efficient recurrent neural network (RNN) for accurate and complete dense point cloud reconstruction. Instead of regularizing the cost volume via conventional 3D CNN or unidirectional RNN like previous attempts, we adopt a bidirectional hybrid Long Short-Term Memory (LSTM) based structure for cost volume regularization. The proposed bidirectional recurrent regularization is able to perceive full-space context information comparable to 3D CNNs while saving runtime memory. For post-processing, we introduce a visibility based approach for depth map refinement to obtain more accurate dense point clouds. Extensive experiments on DTU, Tanks and Temples and ETH3D datasets demonstrate that our method outperforms previous state-of-the-art MVS methods and exhibits high memory efficiency at runtime.

Economies of Scale, Efficiency and Optimal Size of the Hospital

Hospitals are being expanded all over the world. Hospitals are becoming larger (i.e., increasing patient admissions each year) and more complex (i.e., offering a wider range of services to patients with more diverse medical care needs). In the hospital sector and other branches of the economy, economies of scale are important, according to which the volume of medical services can be increased by reducing average costs and increasing quality. However, measuring this relationship between the volume of medical services and costs is difficult because hospitals differ substantially in terms of specialization, patient composition, and other parameters that affect the volume of costs. The paper discusses the features of economies of scale in the healthcare sector and the dependence of the volume of medical services on costs and quality. Studies confirm that the influence of various factors on the costs and quality of medical services depends on the environment and the system of incentives in which hospitals operate. Relationships between patients and healthcare providers based solely on price negotiations do not ensure adequate healthcare quality. Due to informational asymmetry with respect to the real cost of hospital services and quality of treatment, medical service buyers cannot influence cost reductions and increase the quality of treatment, which increases hospital power. Thus, when evaluating the impact of hospital competition on costs and quality of care, we must remember that market mechanisms in health care do not always increase public welfare.

SIM-MultiDepth: Self-Supervised Indoor Monocular Multi-Frame Depth Estimation Based on Texture-Aware Masking

Self-supervised monocular depth estimation methods have become the focus of research since ground truth data are not required. Current single-image-based works only leverage appearance-based features, thus achieving a limited performance. Deep learning based multiview stereo works facilitate the research on multi-frame depth estimation methods. Some multi-frame methods build cost volumes and take multiple frames as inputs at the time of test to fully utilize geometric cues between adjacent frames. Nevertheless, low-textured regions, which are dominant in indoor scenes, tend to cause unreliable depth hypotheses in the cost volume. Few self-supervised multi-frame methods have been used to conduct research on the issue of low-texture areas in indoor scenes. To handle this issue, we propose SIM-MultiDepth, a self-supervised indoor monocular multi-frame depth estimation framework. A self-supervised single-frame depth estimation network is introduced to learn the relative poses and supervise the multi-frame depth learning. A texture-aware depth consistency loss is designed considering the calculation of the patch-based photometric loss. Only the areas where multi-frame depth prediction is considered unreliable in low-texture regions are supervised by the single-frame network. This approach helps improve the depth estimation accuracy. The experimental results on the NYU Depth V2 dataset validate the effectiveness of SIM-MultiDepth. The zero-shot generalization studies on the 7-Scenes and Campus Indoor datasets aid in the analysis of the application characteristics of SIM-MultiDepth.

Enhanced multi view 3D reconstruction with improved MVSNet

Although 3D reconstruction has been widely used in many fields as a key component of environment perception, existing technologies still have the potential for further improvement in 3D scene reconstruction. We propose an improved reconstruction algorithm based on the MVSNet network architecture. To glean richer pixel details from images, we suggest deploying a DE module integrated with a residual framework, which supplants the prevailing feature extraction mechanism. The DE module uses ECA-Net and dilated convolution to expand the receptive field range, performing feature splicing and fusion through the residual structure to retain the global information of the original image. Moreover, harnessing attention mechanisms refines the 3D cost volume's regularization process, bolstering the integration of information across multi-scale feature volumes, consequently enhancing depth estimation precision. When assessed our model using the DTU dataset, findings highlight the network's 3D reconstruction scoring a completeness (comp) of 0.411 mm and an overall quality of 0.418 mm. This performance is higher than that of traditional methods and other deep learning-based methods. Additionally, the visual representation of the point cloud model exhibits marked advancements. Trials on the Blended MVS dataset signify that our network exhibits commendable generalization prowess.

Enhanced feature pyramid for multi-view stereo with adaptive correlation cost volume

Enhanced feature pyramid for multi-view stereo with adaptive correlation cost volume

DeepSFM: Robust Deep Iterative Refinement for Structure From Motion.

Structure from Motion (SfM) is a fundamental computer vision problem which has not been well handled by deep learning. One of the promising solutions is to apply explicit structural constraint, e.g., 3D cost volume, into the neural network. Obtaining accurate camera poses from images alone can be challenging, especially with complicated environmental factors. Existing methods usually assume accurate camera poses from GT or other methods, which is unrealistic in practice and additional sensors are needed. In this work, we design a physical driven architecture, namely DeepSFM, inspired by traditional Bundle Adjustment, which consists of two cost volume based architectures to iteratively refine depth and pose. The explicit constraints on both depth and pose, when combined with the learning components, bring merit from both traditional BA and emerging deep learning technology. To speed up the learning and inference efficiency, we apply the Gated Recurrent Units (GRUs)-based depth and pose update modules with coarse to fine cost volumes on the iterative refinements. In addition, with the extended residual depth prediction module, our model can be adapted to dynamic scenes effectively. Extensive experiments on various datasets show that our model achieves state-of-the-art performance with superior robustness against challenging inputs.

Efficiency-Accuracy Trade-Off in Light Field Estimation with Cost Volume Construction and Aggregation.

The Rich spatial and angular information in light field images enables accurate depth estimation, which is a crucial aspect of environmental perception. However, the abundance of light field information also leads to high computational costs and memory pressure. Typically, selectively pruning some light field information can significantly improve computational efficiency but at the expense of reduced depth estimation accuracy in the pruned model, especially in low-texture regions and occluded areas where angular diversity is reduced. In this study, we propose a lightweight disparity estimation model that balances speed and accuracy and enhances depth estimation accuracy in textureless regions. We combined cost matching methods based on absolute difference and correlation to construct cost volumes, improving both accuracy and robustness. Additionally, we developed a multi-scale disparity cost fusion architecture, employing 3D convolutions and a UNet-like structure to handle matching costs at different depth scales. This method effectively integrates information across scales, utilizing the UNet structure for efficient fusion and completion of cost volumes, thus yielding more precise depth maps. Extensive testing shows that our method achieves computational efficiency on par with the most efficient existing methods, yet with double the accuracy. Moreover, our approach achieves comparable accuracy to the current highest-accuracy methods but with an order of magnitude improvement in computational performance.

Multi-criteria analysis of strategies towards sustainable recycling of phosphorus from sewage sludge in Austria

To promote optimal phosphorus (P) recovery from municipal wastewater and sewage sludge with viable legal instruments, it is imperative to understand the regional and national consequences of different legal requirements for recycling. In this study we develop a scenario-based analysis to assess the environmental and economic impact of different national P recovery strategies in the context of a detailed representation of the existing Austrian wastewater infrastructure. This assessment combines material flow analysis, life cycle assessment and life cycle costing and includes the indicators P recycling rate, P utilization degree, heavy metal removal rate, share of heavy metals’ content in wastewater redirected to agricultural soils, global warming potential, cumulated energy demand, terrestrial acidification potential, volume of freight transport and annual costs. The following main conclusions can be drawn. P recovery from ash shows the highest potential regarding the utilization of P from wastewater. A high P utilization from wastewater should rely on recovery technologies that decontaminate products, otherwise pollutant loads to agricultural soils might increase. P recovery to the extent of 60–85 % of P in WWTPs influent can be achieved by savings/costs of −0.8 to +4.7 EUR inhabitant−1 yr−1 in addition to current cost of the wastewater treatment/sludge disposal system. Key factors to be considered for costs are the choice of recovery process, revenues from products, and the use of existing incineration infrastructure. P recovery can lead to the reduction of greenhouse gas emissions in Austria if nitrous oxide emissions from sludge incineration are limited and efficient heat utilization strategies are implemented. There is a trade-off in terms of environmental and economic costs in choosing a more centralized or decentralized mono-incineration strategy.