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MGSO: Monocular Real-time Photometric SLAM with Efficient 3D Gaussian Splatting

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arXiv 2024年

作者： Hu, Yan Song Abboud, Nicolas Ali, Muhammad Qasim Yang, Adam Srebrnjak Elhajj, Imad Asmar, Daniel Chen, Yuhao Zelek, John S. Vision and Image Processing Lab The Faculty of System Design Engineering The University of Waterloo 200 University Avenue West WaterlooON Canada Vision and Robotics Lab Maroun Semaan Faculty of Engineering and Architecture American University of Beirut Riad El Solh Beirut1107 2020 Lebanon

Real-time SLAM with dense 3D mapping is computationally challenging, especially on resource-limited devices. The recent development of 3D Gaussian Splatting (3DGS) offers a promising approach for real-time dense 3D reconstruction. However, existing 3DGS-based SLAM systems struggle to balance hardware simplicity, speed, and map quality. Most systems excel in one or two of the aforementioned aspects but rarely achieve all. A key issue is the difficulty of initializing 3D Gaussians while concurrently conducting SLAM. To address these challenges, we present Monocular GSO (MGSO), a novel real-time SLAM system that integrates photometric SLAM with 3DGS. Photometric SLAM provides dense structured point clouds for 3DGS initialization, accelerating optimization and producing more efficient maps with fewer Gaussians. As a result, experiments show that our system generates reconstructions with a balance of quality, memory efficiency, and speed that outperforms the state-of-the-art. Furthermore, our system achieves all results using RGB inputs. We evaluate the Replica, TUM-RGBD, and EuRoC datasets against current live dense reconstruction systems. Not only do we surpass contemporary systems, but experiments also show that we maintain our performance on laptop hardware, making it a practical solution for robotics, A/R, and other real-time applications. © 2024, CC BY-NC-SA.

关键词： SLAM robotics

REGION-LEVEL labELS IN ICE CHARTS CAN PRODUCE PIXEL-LEVEL SEGMENTATION FOR SEA ICE TYPES

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arXiv 2024年

作者： Patel, Muhammed Chen, Xinwei Xu, Linlin Chen, Yuhao Scott, K. Andrea Clausi, David A. Vision and Image Processing Lab Systems Design Engineering University of Waterloo Canada School of Marine Science and Engineering South China University of Technology China Department of Mechanical and Mechatronics Engineering University of Waterloo Canada

Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data. In response, our weakly supervised learning method provides a compelling alternative by utilizing lower-resolution regional labels from expert-annotated ice charts. This approach achieves exceptional pixel-level classification performance by introducing regional loss representations during training to measure the disparity between predicted and ice chart-derived sea ice type distributions. Leveraging the AI4Arctic Sea Ice Challenge Dataset, our method outperforms the fully supervised U-Net benchmark, the top solution of the AutoIce challenge, in both mapping resolution and class-wise accuracy, marking a significant advancement in automated operational sea ice mapping. © 2024, CC BY.

关键词： Sea ice

The Influence of Input Variable Selection on Deep Learning-Based Sea Ice Parameter Inversion from Multi-Sensor Satellite Data

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The Influence of Input Variable Selection on Deep Learning-B...

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作者： Xinwei Chen Muhammed Patel Fernando J. Pena Cantu Jinman Park Javier Noa Turnes Mingzhe Jiang Linlin Xu K. Andrea Scott David A. Clausi Weimin Huang Department of System Design Engineering Vision and Image Processing Lab University of Waterloo Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada

The automated mapping of Arctic sea ice using satellite data from various sources plays a critical role in maritime shipping and climate science research. Deep learning models, particularly convolutional neural networks (CNNs), have shown significant potential in generating more accurate and reliable automated sea ice maps from synthetic aperture radar (SAR) images. This study focuses on investigating the impact of integrating multi-source data on enhancing the performance of CNN-based sea ice mapping models. By employing a multitask U-Net architecture, the paper aims to estimate three essential sea ice parameters, namely sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE), through different combinations of input data. The AI4Arctic Sea Ice Challenge dataset with multi-sensor satellite data is utilized for model training and evaluation. The experimental findings confirm the effectiveness of incorporating passive microwave data and spatial-temporal information in conjunction with SAR imagery, as well as feature redundancy removal, resulting in improved accuracy for sea ice parameter estimation.

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COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT images

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arXiv 2022年

作者： Gunraj, Hayden Tuinstra, Tia Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

Computed tomography (CT) has been widely explored as a COVID-19 screening and assessment tool to complement RT-PCR testing. To assist radiologists with CT-based COVID-19 screening, a number of computer-aided systems have been proposed. However, many proposed systems are built using CT data which is limited in both quantity and diversity. Motivated to support efforts in the development of machine learning-driven screening systems, we introduce COVIDx CT-3, a large-scale multinational benchmark dataset for detection of COVID-19 cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068 patients across at least 17 countries, which to the best of our knowledge represents the largest, most diverse dataset of COVID-19 CT images in open-access form. Additionally, we examine the data diversity and potential biases of the COVIDx CT-3 dataset, finding that significant geographic and class imbalances remain despite efforts to curate data from a wide variety of sources. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

Tissue-Specific labelling using Temporal Feature Extraction in Photoacoustic Remote Sensing Microscopy

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Tissue-Specific Labelling using Temporal Feature Extraction ...

Clinical and Translational Biophotonics, Translational 2022

作者： Tweel, James Pellegrino, Nicholas Ecclestone, Benjamin R. Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Vision and Image Processing Lab Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Illumisonics Inc. 22 King Street South WaterlooONN2J 1N8 Canada

Characteristic time-domain (TD) features are extracted from Photoacoustic Remote Sensing TD modulation using a novel temporal clustering algorithm. Features correspond to tissue-specific structures in unprocessed rese... 详细信息

ISBN: (纸本)9781557528209

关键词： Tissue

Pixel-Level Sea Ice Concentration Retrieval from Sentinel-1 SAR imagery and Ancillary Data via Regional Loss Representations

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Pixel-Level Sea Ice Concentration Retrieval from Sentinel-1 ...

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作者： Xinwei Chen Muhammed Patel Linlin Xu Yuhao Chen David A. Clausi K. Andrea Scott Weimin Huang School of Marine Science and Engineering South China University of Technology Department of System Design Engineering Vision and Image Processing Lab University of Waterloo Department of Mechanical & Mechatronics Engineering University of Waterloo Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

The automated monitoring and high resolution mapping of Arctic sea ice concentration (SIC) from synthetic aperture radar (SAR) images are crucial for various purposes, such as tactical navigation. However, the available labels for training deep learning-based SIC estimation models are mostly in coarse spatial resolution. This study tackles this key issue by proposing a novel loss calculation algorithm during model training. Consequently, a U-Net-based model can produce fine-grained SIC estimates via direct learning from ice charts in low resolution. The proposed method is evaluated with the recently release AI4Arctic Sea Ice Challenge Dataset with more than 500 Sentinel-1 SAR imager and associated ice charts. Visual interpretation and numerical results demonstrate the outstanding performance of the proposed method in terms of SIC mapping resolution. The effectiveness of incorporating multi-source ancillary data in performance improvement is also validated.

关键词： Training Visualization Sentinel-1 Estimation Radar polarimetry Numerical models Spatial resolution

Quantization in Relative Gradient Angle Domain For Building Polygon Estimation

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Quantization in Relative Gradient Angle Domain For Building ...

International Conference on Pattern Recognition

作者： Yuhao Chen Yifan Wu Linlin Xu Alexander Wong Vision and Image Processing Lab (VIP) University of Waterloo Waterloo Ontario Canada

Building footprint extraction in remote sensing data benefits many important applications, such as urban planning and population estimation. Recently, rapid development of convolutional neural networks (CNNs) and open-sourced high resolution satellite building image datasets have pushed the performance boundary further for automated building extractions. However, CNN approaches often generate imprecise building morphologies including noisy edges and round corners. In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs. We describe a new transform, Relative Gradient Angle Transform (RGA Transform) that converts object contours from time vs. space to time vs. angle. We propose a new shape descriptor, Boundary Orientation Relation Set (BORS), to describe angle relationship between edges in RGA domain, such as orthogonality and parallelism. Finally, we develop an energy minimization framework that makes use of the angle relationship in BORS to straighten edges and reconstruct sharp corners, and the resulting corners create a polygon. Experimental results demonstrate that our method refines CNN output from a rounded approximation to a more clear-cut angular shape of the building footprint.

关键词： Shape Buildings Urban planning Sociology Estimation Transforms Minimization

Text Detection & Recognition in the Wild for Robot Localization

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arXiv 2022年

作者： Raisi, Zobeir Zelek, John Vision and Image Processing Lab Systems Design Engineering Department University of Waterloo WaterlooONN2L 3G1 Canada

Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due such factors as pose, irregular text, illumination and occlusion. We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. This model is more suitable for VPR. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. To evaluate our proposed architecture’s performance for VPR, we conducted several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The initial experimental results show that the proposed method outperforms the SOTA methods in terms of precision and recall when tested on this benchmark. © 2022, CC BY-NC-SA.

关键词： Robot applications

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray images for Computer-Aided COVID-19 Diagnostics

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arXiv 2022年

作者： Pavlova, Maya Tuinstra, Tia Aboutalebi, Hossein Zhao, Andy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Cheriton School of Computer Science University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained on. Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is composed of 30,386 CXR images from a multinational cohort of 17,026 patients from at least 51 countries, making it, to the best of our knowledge, the most extensive, most diverse COVID-19 CXR dataset in open access form. Here, we provide comprehensive details on the various aspects of the proposed dataset including patient demographics, imaging views, and infection types. The hope is that COVIDx CXR-3 can assist scientists in advancing machine learning research against both the COVID-19 pandemic and related diseases. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19