Publication: International Journal

Jeong, Wonje; Park, Soon-Yong

UGC-Net: Uncertainty-Guided Cost Volume Optimization with Contextual Features for Satellite Stereo Matching Journal Article

In: Remote Sensing, vol. 17, no. 10, 2025, ISSN: 2072-4292.

Abstract | Links | BibTeX

@article{rs17101772,

title = {UGC-Net: Uncertainty-Guided Cost Volume Optimization with Contextual Features for Satellite Stereo Matching},

author = {Wonje Jeong and Soon-Yong Park},

url = {https://www.mdpi.com/2072-4292/17/10/1772},

doi = {10.3390/rs17101772},

issn = {2072-4292},

year  = {2025},

date = {2025-01-01},

journal = {Remote Sensing},

volume = {17},

number = {10},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Park, Sieun; Jeong, Won-Je; Manawadu, Mayura; Park, Soon-Yong

6-DoF Pose Estimation from Single RGB Image and CAD Model Retrieval Using Feature Similarity Measurement Journal Article

In: Applied Sciences, vol. 15, no. 3, 2025, ISSN: 2076-3417.

Abstract | Links | BibTeX

@article{app15031501,

title = {6-DoF Pose Estimation from Single RGB Image and CAD Model Retrieval Using Feature Similarity Measurement},

author = {Sieun Park and Won-Je Jeong and Mayura Manawadu and Soon-Yong Park},

url = {https://www.mdpi.com/2076-3417/15/3/1501},

doi = {10.3390/app15031501},

issn = {2076-3417},

year  = {2025},

date = {2025-01-01},

journal = {Applied Sciences},

volume = {15},

number = {3},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Seo, Dong-Uk; Park, Soon-Yong

DSM Reconstruction from Uncalibrated Multi-View Satellite Stereo Images by RPC Estimation and Integration Journal Article

In: Remote Sensing, vol. 16, no. 20, 2024, ISSN: 2072-4292.

Abstract | Links | BibTeX

@article{rs16203863,

title = {DSM Reconstruction from Uncalibrated Multi-View Satellite Stereo Images by RPC Estimation and Integration},

author = {Dong-Uk Seo and Soon-Yong Park},

url = {https://www.mdpi.com/2072-4292/16/20/3863},

doi = {10.3390/rs16203863},

issn = {2072-4292},

year  = {2024},

date = {2024-01-01},

journal = {Remote Sensing},

volume = {16},

number = {20},

abstract = {In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Manawadu, Mayura; Park, Soon-Yong

6DoF Object Pose and Focal Length Estimation from Single RGB Images in Uncontrolled Environments Journal Article

In: Sensors, vol. 24, no. 17, 2024, ISSN: 1424-8220.

Abstract | Links | BibTeX

@article{s24175474,

title = {6DoF Object Pose and Focal Length Estimation from Single RGB Images in Uncontrolled Environments},

author = {Mayura Manawadu and Soon-Yong Park},

url = {https://www.mdpi.com/1424-8220/24/17/5474},

doi = {10.3390/s24175474},

issn = {1424-8220},

year  = {2024},

date = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {17},

abstract = {Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis (tz) and the camera’s focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage, tz is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed tz. In the second stage, we predict the true value of tz while scaling the focal length based on the tz update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis (tz) and the camera’s focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage, tz is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed tz. In the second stage, we predict the true value of tz while scaling the focal length based on the tz update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method.

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Park, Soon-Yong; Lee, Junesuk

TDO-SLAM: Traffic Sign and Dynamic Object based Visual SLAM Journal Article

In: IEEE Access, 2024.

Links | BibTeX