Cost Volume Research Articles

Cost Volume Profit Analysis in Procurement of New Machinery as a Basis for Margin Contribution at PT. X in Jakarta

Cost Volume Profit Analysis in Procurement of New Machinery as a Basis for Margin Contribution at PT. X in Jakarta

Towards accurate binocular vision of satellites: A cascaded multi-scale pyramid network for stereo matching on satellite imagery

Stereo matching of high-resolution satellite stereo images is a fundamental and challenging task in photogrammetry, geoscience, and remote sensing. Some deep learning-based methods have shown great potential in solving this task recently. However, these methods have not attracted much attention as their accuracy is still unsatisfactory, especially in regions with intractable factors (e.g., occlusions, repetitive patterns, low texture, non-texture, and disparity discontinuities). To tackle these challenging factors and improve disparity accuracy, we propose an end-to-end stereo matching network named Cascaded Multi-Scale Pyramid Network (CMSP-Net). Firstly, to fully utilize the multi-level spatial context information and mitigate the ambiguous matching caused by repetitive patterns, low texture, non-texture, and occlusions, a cost volume pyramid is constructed with multi-scale image features. Then, each cost volume in the pyramid is aggregated with an hourglass structure to exploit the multi-scale context in a single cost volume. Secondly, considering that high-resolution cost volumes contain more detailed information but have small receptive fields, while low-resolution ones have large receptive fields but lack details, we design an Attention-Guided Cost Fusion (AGCF) strategy to fuse pyramid cost volumes, which effectively integrates the advantages of multi-scale cost volumes while mitigating the backfiring caused by semantic inconsistency across different scales. Thirdly, a disparity refinement module is designed to improve overall disparity accuracy further, which incorporates the reconstruction error and edge clues of input satellite stereo images. Extensive experimental results on multiple satellite datasets demonstrate that the proposed method can effectively reduce matching ambiguities and recover sharp disparity discontinuities, achieving clear superiority over existing state-of-the-art methods.

Robust distance measurement using illumination map estimation and MAHNet in underground coal mines

An effective binocular stereo distance measurement method is proposed to address challenges posed by low brightness and weak texture of images captured in underground coal mines for the machine vision method. This approach is based on illumination map estimation and the MobileNetV3 attention hourglass stereo matching network (MAHNet) model. First, a binocular stereo vision system is established in which infrared LEDs are uniformly distributed on both sides of the belt conveyor bracket as visual feature points. Second, images are preprocessed using illumination map estimation, and the optimization of inhomogeneous brightness image enhancement is achieved by adopting adaptive Gamma correction. Third, the YOLOv5 target detection network and Gaussian fitting fusion algorithm are utilized to detect infrared LED feature points. Fourth, the MAHNet model is employed to generate the cost volume and perform disparity regression, resulting in the acquisition of accurate disparity images. Finally, triangulation is applied to determine the depth of feature points. The experimental results of distance measurement demonstrate that an average relative ranging accuracy of 1.52% within the range of 50.0 cm to 250.0 cm can be achieved by the optimized method, thereby validating the effectiveness of this binocular distance measurement method in underground coal mines.

Zomato’s Quest for Survival

The case of ‘Zomato’s Quest for Survival’ focuses on the concept of value relevance of accounting information. The case deals with financial ratio analysis, comparative financial statement analysis, cost–volume–profit analysis and DuPont analysis. The case also deals with the effect of acquisition on the acquiring firm’s fundamentals, post-acquisition. The case explores the potential growth of India’s food delivery market and goes on to explore Zomato’s business strategy and source of income. The case starts with Mr. Arghadip considering purchasing Zomato Ltd. Shares. He, therefore, gathered information from various sources to assess the company’s fundamentals and prospects for the future. A lot of questions worth discussing were brought up by the case. The ideal students for the case would be students of B.Com, M.Com, BBA and MBA. The case can also be discussed in Chartered Accountant and Cost and Management Accountant courses.

Hourglass cascaded recurrent stereo matching network

Stereo matching acts a crucial role in computer vision and robotics applications. An accurate cost volume and robust disparity regression method are essential for stereo matching of high accuracy. Following GCNet and PSMNet, constructing 4D cost volume and then using the soft argmin method to regress has been dominated. However, it will encounter many difficulties due to the multi-modal distribution of cost volume. One of the reasons for this multi-modal distribution is the occlusion area which not be possible to find a matching region on the reference image and rarely discussed. In this paper, we propose to use global context information could improve the performance of model in occluded regions. Recently, novel recurrent neural network regression methods are proposed, but most of them regress disparity maps from 3D cost volume. In this paper, we propose the new combinatorial paradigm that combine stacked hourglass modules and recurrent neural networks to further aggregate 4D cost volume and regress disparity respectively. The proposed method can be seamlessly integrated into most stereo matching networks, we improved the accuracy by 45% for PSMNet and 38% for GwcNet in our experiment. Experimental results on Scene Flow, KITTI2012, KITTI2015, and ETH3D datasets show our method is competitive. The code is available at: https://github.com/truman1211/HCRnet.

Stereo-DICNet: An efficient and unified speckle matching network for stereo digital image correlation measurement

Digital image correlation (DIC) is an image-based, non-contact optical measurement technique for measuring the deformation of objects. Recently, deep learning has been preliminarily used in monocular-DIC measurement and has brought significant advantages in efficiency and robustness. However, the deep learning-based stereo-DIC methods is still underexplored, although stereo-DIC can measure three-dimensional deformation and shape compared to monocular-DIC. Different from monocular-DIC, stereo-DIC not only requires temporal matching (inter-frame) but also stereo matching (inter-camera), and the difference in image deformation scale between the two matching tasks make it difficult to perform them using a unified network. To solve this problem, we propose a unified speckle matching network that can be used for deformation measurement at different scales, referred to as Stereo-DICNet. Stereo-DICNet consists of three modules: feature extraction, cost volume generation, and displacement map prediction. Firstly, the feature extraction module composed of the residual layer and the spatial pyramid pooling layer is designed to extract the features of different receptive fields. Then, an attention connect volume was proposed to represent the similarity information between pixels at different spatial scales. Finally, the displacement map is predicted by a stacked hourglass-shaped sub-net. Simulation and physical experiments verify that Stereo-DICNet can perform both temporal matching and stereo matching. Moreover, material tensile experiments show that the MAE of Stereo-DICNet in the U, V, and W directions are no more than 0.005 mm, 0.015 mm, 0.01 mm respectively.

Learning Bilateral Cost Volume for Rolling Shutter Temporal Super-Resolution.

Rolling shutter temporal super-resolution (RSSR), which aims to synthesize intermediate global shutter (GS) video frames between two consecutive rolling shutter (RS) frames, has made remarkable progress with the development of deep convolutional neural networks over the past years. Existing methods cascade multiple separated networks to sequentially estimate intermediate motion fields and synthesize target GS frames. Nevertheless, they are typically complex, do not facilitate the interaction of complementary motion and appearance information, and suffer from problems such as pixel aliasing or poor interpretation. In this paper, we derive the uniform bilateral motion fields for RS-aware backward warping, which endows our network a more explicit geometric meaning by injecting spatio-temporal consistency information through time-offset embedding. More importantly, we develop a unified, single-stage RSSR pipeline to recover the latent GS video in a coarse-to-fine manner. It first extracts pyramid features from given inputs, and then refines the bilateral motion fields together with the anchor frame until generating the desired output. With the help of our proposed bilateral cost volume, which uses the anchor frame as a common reference to model the correlation with two RS frames, the gradually refined anchor frames not only facilitate intermediate motion estimation, but also compensate for contextual details, making additional frame synthesis or refinement networks unnecessary. Meanwhile, an asymmetric bilateral motion model built on top of the symmetric bilateral motion model further improves the generality and adaptability, yielding better GS video reconstruction performance. Extensive quantitative and qualitative experiments on synthetic and real data demonstrate that our method achieves new state-of-the-art results.

Adaptively identify and refine ill-posed regions for accurate stereo matching

Stereo matching cost constrains the consistency between pixel pairs. However, the consistency constraint becomes unreliable in ill-posed regions such as occluded or ambiguous regions of the images, making it difficult to explore hidden correspondences. To address this challenge, we introduce an Error-area Feature Refinement Mechanism (EFR) that supplies context features for ill-posed regions. In EFR, we innovatively obtain the suspected error region according to aggregation perturbations, then a simple Transformer module is designed to synthesize global context and correspondence relation with the identified error mask. To better overcome existing texture overfitting, we put forward a Dual-constraint Cost Volume (DCV) that integrates supplementary constraints. This effectively improves the robustness and diversity of disparity clues, resulting in enhanced details and structural accuracy. Finally, we propose a highly accurate stereo matching network called Error-rectify Feature Guided Stereo Matching Network (ERCNet), which is based on DCV and EFR. We evaluate our model on several benchmark datasets, achieving state-of-the-art performance and demonstrating excellent generalization across datasets. The code is available at https://github.com/dean7liu/ERCNet_2023.

Teacher-student guided knowledge distillation for unsupervised convolutional neural network-based speckle tracking in ultrasound strain elastography.

Accurate and efficient motion estimation is a crucial component of real-time ultrasound elastography (USE). However, obtaining radiofrequency ultrasound (RF) data in clinical practice can be challenging. In contrast, although B-mode (BM) data is readily available, elastographic data derived from BM data results in sub-optimal elastographic images. Furthermore, existing conventional ultrasound devices (e.g., portable devices) cannot provide elastography modes, which has become a significant obstacle to the widespread use of traditional ultrasound devices. To address the challenges above, we developed a teacher-student guided knowledge distillation for an unsupervised convolutional neural network (TSGUPWC-Net) to improve the accuracy of BM motion estimation by employing a well-established convolutional neural network (CNN) named modified pyramid warping and cost volume network (MPWC-Net). A pre-trained teacher model based on RF is utilized to guide the training of a student model using BM data. Innovations outlined below include employing spatial attention transfer at intermediate layers to enhance the guidance effect of the model. The loss function consists of smoothness of the displacement field, knowledge distillation loss, and intermediate layer loss. We evaluated our method on simulated data, phantoms, and in vivo ultrasound data. The results indicate that our method has higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values in axial strain estimation than the model trained on BM. The model is unsupervised and requires no ground truth labels during training, making it highly promising for motion estimation applications.

Accurate and Efficient Stereo Matching via Attention Concatenation Volume.

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this article, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. The ACV can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy. We further design a fast version of ACV to enable real-time performance, named Fast-ACV, which generates high likelihood disparity hypotheses and the corresponding attention weights from low-resolution correlation clues to significantly reduce computational and memory cost and meanwhile maintain a satisfactory accuracy. Furthermore, we design a highly accurate network ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV respectively, which achieve state-of-the-art performance on several benchmarks.

Modeling Stereo-Confidence out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep

We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems. Grounded in the foundational concepts of disparity definition and the disparity plane sweep, the proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. Based on this idea, the proposed stereo-confidence method can be summarized in three folds. 1) Using the disparity plane sweep, multiple disparity maps can be obtained and treated as a 3-D volume (predicted disparity volume), like the cost volume is constructed. 2) One of these disparity maps serves as an anchor, allowing us to define a desirable (or ideal) disparity profile at every spatial point. 3) By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement. Extensive experimental results using various stereo-matching networks and datasets demonstrate that the proposed stereo-confidence method not only shows competitive performance on its own but also consistent performance improvements when it is used as an input modality for learning-based stereo-confidence methods.

BSI-MVS: multi-view stereo network with bidirectional semantic information

The basic principle of multi-view stereo (MVS) is to perform 3D reconstruction by extracting depth information from multiple views. Most current SOTA MVS networks are based on Vision Transformer, which usually means expensive computational complexity. To reduce computational complexity and improve depth map accuracy, we propose a MVS network with Bidirectional Semantic Information (BSI-MVS). Firstly, we design a Multi-Level Spatial Pyramid module to generate multiple layers of feature map for extracting multi-scale information. Then we propose a 2D Bidirectional-LSTM module to capture bidirectional semantic information at different time steps in the horizontal and vertical directions, which contains abundant depth information. Finally, cost volumes are built based on various levels of feature maps to optimize the final depth map. We experiment on the DTU and BlendedMVS datasets. The result shows that our network, in terms of overall metrics, surpasses TransMVSNet, CasMVSNet, CVP-MVSNet, and AACVP-MVSNet respectively by 17.84%, 36.42%, 14.96%, and 4.86%, which also shows a noticeable performance enhancement in objective metrics and visualizations.

U-ETMVSNet: Uncertainty-Epipolar Transformer Multi-View Stereo Network for Object Stereo Reconstruction

The Multi-View Stereo model (MVS), which utilizes 2D images from multiple perspectives for 3D reconstruction, is a crucial technique in the field of 3D vision. To address the poor correlation between 2D features and 3D space in existing MVS models, as well as the high sampling rate required for static sampling, we proposeU-ETMVSNet in this paper. Initially, we employ an integrated epipolar transformer module (ET) to establish 3D spatial correlations along epipolar lines, thereby enhancing the reliability of aggregated cost volumes. Subsequently, we devise a sampling module based on probability volume uncertainty to dynamically adjust the depth sampling range for the next stage. Finally, we utilize a multi-stage joint learning method based on multi-depth value classification to evaluate and optimize the model. Experimental results demonstrate that on the DTU dataset, our method achieves a relative performance improvement of 27.01% and 11.27% in terms of completeness error and overall error, respectively, compared to CasMVSNet, even at lower depth sampling rates. Moreover, our method exhibits excellent performance with a score of 58.60 on the Tanks &Temples dataset, highlighting its robustness and generalization capability.

UGNet: Uncertainty aware geometry enhanced networks for stereo matching

Stereo matching is a fundamental research area in the field of computer vision. In recent years, iterative methods based on Gated Recurrent Units (GRUs) have showcased remarkable achievements in this domain. Despite their high accuracy, these methods suffer from notable limitations such as a reliance on a large number of iterations and a tendency to lose high-frequency details. To address these issues, we propose a novel uncertainty-aware framework that combines 3D convolution and GRU-based iterations, aiming to improve efficiency and accuracy. Specifically, we first introduce a probabilistic method to jointly train the disparity map and its corresponding uncertainty map using 3D convolutions. Next, leveraging the uncertainty map as a guide, we develop a novel uncertainty reweighting iterative module to assist in identifying errors in the coarse disparity and cost volume, thereby refining the disparity estimation process and significantly improving the iteration efficiency. Moreover, we introduce a high-resolution refinement module that utilizes Pixel Difference Convolution (PDC) to incorporate additional gradient information. This module can fine-tune the disparity estimation to enhance accuracy. Finally, our network is evaluated on multiple widely-used benchmark datasets. The results demonstrate its proficiency in predicting precise boundaries and effectively reduce iterations. Our model achieves comparable performance to other state-of-the-art methods, ranking 1st on KITTI 2015, and 2nd on KITTI 2012. These results validate its strong performance and generalizability.

Feature‐enhanced representation with transformers for multi‐view stereo

AbstractMost existing multi‐view stereo (MVS) methods fail to consider global context information in the stage of feature extraction and cost aggregation. As transformers have shown remarkable performance on various vision tasks due to their ability to perceive global contextual information, this paper proposes a transformer‐based feature enhancement network (TF‐MVSNet) to facilitate feature representation learning by combining local features (both 2D and 3D) with long‐range contextual information. To reduce memory consumption of feature matching, the cross‐attention mechanism is leveraged to efficiently construct 3D cost volumes under the epipolar constraint. Additionally, a colour‐guided network is designed to refine depth maps at a coarse stage, hence reducing incorrect depth predictions at a fine stage. Extensive experiments were performed on the DTU dataset and Tanks and Temples (T&T) benchmark and results are reported.

End-to-End Edge-Guided Multi-Scale Matching Network for Optical Satellite Stereo Image Pairs

Acquiring disparity maps by dense stereo matching is one of the most important methods for producing digital surface models. However, the characteristics of optical satellite imagery, including significant occlusions and long baselines, increase the challenges of dense matching. In this study, we propose an end-to-end edge-guided multi-scale matching network (EGMS-Net) tailored for optical satellite stereo image pairs. Using small convolutional filters and residual blocks, the EGMS-Net captures rich high-frequency signals during the initial feature extraction phase. Subsequently, pyramid features are derived through efficient down-sampling and consolidated into cost volumes. To regularize these cost volumes, we design a top–down multi-scale fusion network that integrates an attention mechanism. Finally, we innovate the use of trainable guided filter layers in disparity refinement to improve edge detail recovery. The network is trained and evaluated using the Urban Semantic 3D and WHU-Stereo datasets, with subsequent analysis of the disparity maps. The results show that the EGMS-Net provides superior results, achieving endpoint errors of 1.515 and 2.459 pixels, respectively. In challenging scenarios, particularly in regions with textureless surfaces and dense buildings, our network consistently delivers satisfactory matching performance. In addition, EGMS-Net reduces training time and increases network efficiency, improving overall results.

ПАРАМЕТРИЧЕСКИЕ МЕТОДЫ ЦЕНООБРАЗОВАНИЯ: МЕТОДИКА И ПРАКТИКА ПРИМЕНЕНИЯ

The scientific article contains a review of economic publications presenting scientific research by domestic and foreign scientists on the problems of pricing in the agricultural sector of the economy: establishing the optimal level of purchase, wholesale and retail prices for agricultural products, ensuring the growth of financial results by increasing income from the sale of products, identifying the dynamics food market, forecasting price dynamics in agricultural production, methodology of pricing policy and price relations of agricultural organizations. A production feasibility study was conducted for the expected sales volume. The influence of sales timing (first batches or the main harvest of agricultural products) and price on the amount of demand (expected sales volume) is shown. A function has been constructed for the dependence of sales volume on the price of agricultural products, clearly showing that an increase in sales volume is possible if the price of agricultural products decreases. It is indicated that at the beginning of the season, a high price repels the consumer, this negatively affects the amount of revenue. A paired regression equation was constructed, and the expected sales volume was determined using regression analysis. The methodology for calculating the total costs of production, their classification depending on the degree of variability (the influence of the expected sales volume) is shown. It is noted that in total costs there is a constant part (fixed costs that do not depend on the expected sales volume) and a variable part (variable costs that change in direct proportion to changes in the expected sales volume). A profit generation model is described, including data from a regression analysis of the expected sales volume and calculation of the total costs of production.

A Light Multi-View Stereo Method with Patch-Uncertainty Awareness.

Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image's feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods.

Unsupervised 3D reconstruction method based on multi-view propagation

In this paper, an end-to-end deep learning framework for reconstructing 3D models by computing depth maps from multiple views is proposed. An unsupervised 3D reconstruction method based on multi-view propagation is introduced, which addresses the issues of large GPU memory consumption caused by most current research methods using 3D convolution for 3D cost volume regularization and regression to obtain the initial depth map, as well as the difficulty in obtaining true depth values in supervised methods due to device limitations. The method is inspired by the Patchmatch algorithm, and the depth is divided into n layers within the depth range to obtain depth hypotheses through multi-view propagation. What's more, a multi-metric loss function is constructed based on luminosity consistency, structural similarity, and depth smoothness between multiple views to serve as a supervisory signal for learning depth predictions in the network. The experimental results show our proposed method has a very competitive performance and generalization on the DTU, Tanks & Temples and our self-made dataset; Specifically, it is at least 1.7 times faster and requires more than 75% less memory than the method that utilizes 3D cost volume regularization.

Analisis Cost Volume Profit Sebagai Dasar Perencanaan Laba Yang Diharapkan

Cost Volume Profit Analysis as a Basis for Expected Profit Planning (Case Study of Privately Owned Business "Ifamoy Home Industri"). Under the guidance of A company needs profit planning to help management determine the level of profit it wants to obtain, with Cost Volume Profit analysis which focuses on various factors that influence changes in profit components at the Ifamoy Home Industry company. This research aims to determine the application of Cost Volume Profit Analysis in the Ifamoy Home Industry. This research uses descriptive research. The data collection technique used is the Observation and Documentation Technique on existing income data at Ifamoy Home Industry. The results of this research show that the Total Calculation Profit is IDR. 828,000,0000.00, with a total Contribution Margin of IDR. 1,670,000,000 with a ratio of 0.8%, with a Total Break Even Point of Rp. 512,000,000, with a total margin of safety of Rp. 1,568,000,000, with a ratio of 75%, with profit planning to increase by (20%) in 2023 resulting in a profit of Rp. 2,508,200,000.