Disparity estimation in satellite stereo images is a highly challenging task due to complex terrain, occlusions caused by tall buildings and structures, and texture-less regions such as roads, rivers, and building roofs. Recent deep learning-based satellite stereo disparity estimation methods have adopted cascade multi-scale feature extraction techniques to address these challenges. However, the recent learning-based methods still struggle to effectively estimate disparity in the high ambiguity regions. This paper proposes a disparity estimation and refinement method that leverages variance uncertainty in the cost volume to overcome these limitations. The proposed method calculates variance uncertainty from the cost volume and generates uncertainty weights to adjust the cost volume based on this information. These weights are designed to emphasize geometric features in regions with low uncertainty while enhancing contextual features in regions with high uncertainty, such as occluded or texture-less areas. Furthermore, the proposed method introduces a pseudo volume, referred to as the 4D context volume, which extends the reference image’s features during the stereo-matching aggregation step. By integrating the 4D context volume into the aggregation layer of the geometric cost volume, our method effectively addresses challenges in disparity estimation, particularly in occluded and texture-less areas. For the evaluation of the proposed method, we use the Urban Semantic 3D dataset and the WHU-Stereo dataset. The evaluation results show that the proposed method achieves state-of-the-art performance, improving disparity accuracy in challenging regions.

This study presents six degrees of freedom (6-DoF) pose estimation of an object from a single RGB image and retrieval of the matching CAD model by measuring the similarity between RGB and CAD rendering images. The 6-DoF pose estimation of an RGB object is one of the important techniques in 3D computer vision. However, in addition to 6-DoF pose estimation, retrieval and alignment of the matching CAD model with the RGB object should be performed for various industrial applications such as eXtended Reality (XR), Augmented Reality (AR), robot’s pick and place, and so on. This paper addresses 6-DoF pose estimation and CAD model retrieval problems simultaneously and quantitatively analyzes how much the 6-DoF pose estimation affects the CAD model retrieval performance. This study consists of two main steps. The first step is 6-DoF pose estimation based on the PoseContrast network. We enhance the structure of PoseConstrast by adding variance uncertainty weight and feature attention modules. The second step is the retrieval of the matching CAD model by an image similarity measurement between the CAD rendering and the RGB object. In our experiments, we used 2000 RGB images collected from Google and Bing search engines and 100 CAD models from ShapeNetCore. The Pascal3D+ dataset is used to train the pose estimation network and DELF features are used for the similarity measurement. Comprehensive ablation studies about the proposed network show the quantitative performance analysis with respect to the baseline model. Experimental results show that the pose estimation performance has a positive correlation with the CAD retrieval performance.

This paper proposes a 6-DoF pose estimation and CAD model retrieval for XR interface from a single RGB image. A deep learning network is used to estimate the 6-DoF pose of a real object in an RGB image. Then, several CAD model candidates are rendered and their similarity with the image is measured to decide the final matching CAD model. In the last, the rendered image of the matching CAD model is overlayed with the RGB image. In the experiment, total ten object categories are tested and evaluated using two deep networks.

Fluctuating pressure generated during ship operation is closely related to propeller vibration, noise, and structural safety, and its frequency spectrum is a key design indicator in the propeller design stage. However, water tunnel experiments to measure fluctuating pressure generated by high-speed propellers require high-pressure facilities and involve complex procedures, high costs, and long lead times when experimental conditions are modified or new propellers are tested. To overcome these limitations, this study proposes a deep learning-based network, referred to as MSPFS-Net (Model-Test Ship Pressure Frequency Spectra Network), to estimate the frequency spectrum of fluctuating pressure from model test data. The proposed method uses propeller CAD data, principal design parameters, wake data, and water tunnel test conditions as inputs, and is trained in a supervised learning framework using frequency-domain data obtained by transforming experimentally measured fluctuating pressure signals. The trained network can predict the fluctuating pressure frequency spectrum without direct sensor measurements, even under conditions not present in the training dataset. The results of this study demonstrate the potential to reduce dependence on water tunnel experiments and to efficiently evaluate fluctuating pressure characteristics in the early design stage, indicating that the proposed approach can serve as a practical design support tool in terms of both cost and time efficiency.

In remote sensing, disparity calculation using stereo images is a very necessary task and provides information for estimating the terrain elevation. The fields using disparity of stereo satellite images are used in various fields such as terrain models, autonomous driving using 3D maps, and content development. However, extracting disparity from stereo satellite images is a very difficult task, and inaccurate disparity may be extracted due to complex environments, façade areas of buildings, and texture-less areas. Our proposed method improves feature extraction and 3D aggregation steps based on Gwc-Net using stereo images rectified through RPC (Rational Polynomial Coefficients). To this achieve, we first improve the accuracy of the initial cost volume by extracting important features using the attention module 2D CBAM. In addition, in the aggregation step, we use 3D CBAM to extract important features from the cost volume and use GCE (Correlate-and-Excite) to guide image features to the cost volume to improve disparity. To evaluate the proposed method, the accuracy of disparity is evaluated using RPC-corrected stereo satellite images of DFC2019 data track2 of the US3D dataset. As a result of the experiment, the proposed method exhibited improvement compared to the baseline Gwc-Net.