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Depth Estimation using Weighted-loss and Transfer learning

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arXiv 2024年

作者： Hafeez, Muhammad Adeel Madden, Michael G. Sistu, Ganesh Ullah, Ihsan Machine Learning Research Group School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Valeo Vision Systems Tuam Ireland

Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance metrics. In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function. The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM). We use a grid search and a random search method to find optimized weights for the losses, which leads to an improved model. We explore multiple encoder-decoder-based models including DenseNet121, DenseNet169, DenseNet201, and EfficientNet for the supervised depth estimation model on NYU Depth Dataset v2. We observe that the EfficientNet model, pre-trained on ImageNet for classification when used as an encoder, with a simple upsampling decoder, gives the best results in terms of RSME, REL and log10: 0.386, 0.113 and 0.049, respectively. We also perform a qualitative analysis which illustrates that our model produces depth maps that closely resemble ground truth, even in cases where the ground truth is flawed. The results indicate significant improvements in accuracy and robustness, with EfficientNet being the most successful architecture. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal encoding

FrontierNet: learning Visual Cues to Explore

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IEEE Robotics and Automation Letters 2025年第7期10卷 6576-6583页

作者： Sun, Boyang Chen, Hanzhi Leutenegger, Stefan Cadena, Cesar Pollefeys, Marc Blum, Hermann ETH Zurich Computer Vision and Geometry Group Zurich8092 Switzerland Technical University of Munich Mobile Robotics Lab München80333 Germany ETH Zurich Mobile Robotics Lab Zurich8092 Switzerland ETH Zurich Robotic Systems Lab Zurich8092 Switzerland AI Lab Microsoft Mixed Reality Zurich8038 Switzerland University of Bonn Lamarr Institute for ML and AI Robot Perception and Learning Lab Bonn53115 Germany

Exploration of unknown environments is crucial for autonomous robots;it allows them to actively reason and decide on what new data to acquire for different tasks, such as mapping, object discovery, and environmental assessment. Existing solutions, such as frontier-based exploration approaches, rely heavily on 3D map operations, which are limited by map quality and, more critically, often overlook valuable context from visual cues. This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map. We propose a visual-only frontier-based exploration system, with FrontierNet as its core component. FrontierNet is a learning-based model that (i) proposes frontiers, and (ii) predicts their information gain, from posed RGB images enhanced by monocular depth priors. Our approach provides an alternative to existing 3D-dependent goal-extraction approaches, achieving a 15% improvement in early-stage exploration efficiency, as validated through extensive simulations and real-world experiments. © 2016 IEEE.

关键词： Deep learning

Mask3D: Mask Transformer for 3D Semantic Instance Segmentation

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arXiv 2022年

作者： Schult, Jonas Engelmann, Francis Hermans, Alexander Litany, Or Tang, Siyu Leibe, Bastian Computer Vision Group RWTH Aachen University Germany Computer Vision and Learning Group ETH Zürich Switzerland ETH AI Center Zürich Switzerland NVIDIA Santa Clara United States

Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques. Building on the successes of recent Transformer-based methods for object detection and image segmentation, we propose the first Transformer-based approach for 3D semantic instance segmentation. We show that we can leverage generic Transformer building blocks to directly predict instance masks from 3D point clouds. In our model – called Mask3D – each object instance is represented as an instance query. Using Transformer decoders, the instance queries are learned by iteratively attending to point cloud features at multiple scales. Combined with point features, the instance queries directly yield all instance masks in parallel. Mask3D has several advantages over current state-of-the-art approaches, since it neither relies on (1) voting schemes which require hand-selected geometric properties (such as centers) nor (2) geometric grouping mechanisms requiring manually-tuned hyper-parameters (e.g. radii) and (3) enables a loss that directly optimizes instance masks. Mask3D sets a new state-of-the-art on ScanNet test (+ 6.2 mAP), S3DIS 6-fold (+ 10.1 mAP), STPLS3D (+ 11.2 mAP) and ScanNet200 test (+ 12.4 mAP). © 2022, CC BY.

关键词： Semantics

Blessemflood21: Advancing Flood Analysis with a High-Resolution Georeferenced Dataset for Humanitarian Aid Support

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Blessemflood21: Advancing Flood Analysis with a High-Resolut...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. computer vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.

关键词： Deep learning Training Semantic segmentation Human in the loop Sensors Floods Task analysis

Fréchet Wavelet Distance: A Domain-Agnostic Metric for Image Generation

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arXiv 2023年

作者： Veeramacheneni, Lokesh Wolter, Moritz Kuehne, Hildegard Gall, Juergen High-Performance Computing and Analytics Lab University of Bonn Bonn53115 Germany Multimodal Learning Group University of Bonn Bonn53115 Germany Computer Vision Group University of Bonn Bonn53115 Germany

Modern metrics for generative learning like Fréchet Inception Distance (FID) demonstrate impressive performance. However, they suffer from various shortcomings, like a bias towards specific generators and datasets. To address this problem, we propose the Fréchet Wavelet Distance (FWD) as a domain-agnostic metric based on Wavelet Packet Transform (Wp). FWD provides a sight across a broad spectrum of frequencies in images with a high resolution, along with preserving both spatial and textural aspects. Specifically, we use Wp to project generated and dataset images to packet coefficient space. Further, we compute Fréchet distance with the resultant coefficients to evaluate the quality of a generator. This metric is general-purpose and dataset-domain agnostic, as it does not rely on any pre-trained network while being more interpretable because of frequency band transparency. We conclude with an extensive evaluation of a wide variety of generators across various datasets that the proposed FWD is able to generalize and improve robustness to domain shift and various corruptions compared to other metrics. © 2023, CC BY.

关键词： Quality control

Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research

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arXiv 2024年

作者： Shukla, Vandita Morelli, Luca Trybala, Pawel Remondino, Fabio Gan, Wentian Yu, Yifei Wang, Xin Trento Italy Computer Vision and Machine Learning Systems Group Institute for Geoinformatics University of Muenster Germany Dept. of Civil Environmental and Mechanical Engineering University of Trento Italy School of Geodesy and Geomatics Wuhan University China

UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions. High-quality real-time scene reconstruction as well as real-time UAV localization can optimize the exploration vs exploitation balance of single or collaborative mission. In this work, we explore the potential of two collaborative frameworks - Visual Simultaneous Localization and Mapping (V-SLAM) and Structure-from-Motion (SfM) for 3D mapping purposes and compare results with standard offline approaches. © 2024, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

BLESSEMFLOOD21: ADVANCING FLOOD ANALYSIS WITH A HIGH-RESOLUTION GEOREFERENCED DATASET FOR HUMANITARIAN AID SUPPORT

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arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

关键词： Floods

learning-based Relational Object Matching Across Views

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Learning-based Relational Object Matching Across Views

IEEE International Conference on Robotics and Automation (ICRA)

作者： Cathrin Elich Iro Armeni Martin R. Oswald Marc Pollefeys Joerg Stueckler Embodied Vision Group Max Planck Institute for Intelligent Systems Tuebingen Germany Max Planck ETH Center for Learning Systems Computer Vision and Geometry Lab ETH Zurich Switzerland University of Amsterdam Netherlands Microsoft Mixed Reality and AI Lab Zurich Switzerland

Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reconstruction, image retrieval, or place recognition can benefit from reasoning on the level of objects. While keypoint-based matching can yield strong results for finding correspondences for images with small to medium view point changes, for large view point changes, matching semantically on the object-level becomes advantageous. In this paper, we propose a learning-based approach which combines local keypoints with novel object-level features for matching object detections between RGB images. We train our object-level matching features based on appearance and inter-frame and cross-frame spatial relations between objects in an associative graph neural network. We demonstrate our approach in a large variety of views on realistically rendered synthetic images. Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.

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Achachay App: a community-driven innovation for flood data collection in urban and rural areas

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Achachay App: a community-driven innovation for flood data c...

International Conference on Technological Innovation and AI Research (ICTIAIR 2025)

作者： Ariana Deyaneira Jiménez-Narváez Jonathan Javier Loor-Duque Diego Josue Andrade-Pelaez Manuel Eugenio Morocho-Cayamcela Raisa Torres-Ramírez Deep Learning for Autonomous Driving Robotics and Computer Vision Research Group (DeepARC) School of Mathematical and Computational Sciences Yachay Tech University San Miguel de Urcuquí Ecuador Applied Geology and Research Group School of Geological Sciences Energy and Environment Yachay Tech University San Miguel de Urcuquí Ecuador

ISBN: (数字)9781837243235

Floods are among the most frequent and devastating natural disasters, significantly impacting urban and rural communities worldwide. Achachay App is a mobile application designed to enhance flood data collection and analysis through direct community engagement. The app enables users to report real-time flood events by sharing detailed descriptions, geographic coordinates, and multimedia evidence such as photos or videos. Researchers use these community-sourced data to analyze and reconstruct watercourse behaviors that lead to flooding. Ecuador was selected for implementation as a pilot initiative, where the app successfully mapped flood-prone areas and secondary flows. These features aim to deepen understanding of the interplay between geological and climatic phenomena, supporting disaster preparedness and prevention. By bridging technology and community collaboration, the Achachay Appoffers valuable insights for policymakers to design sustainable public policies that mitigate risks and promote social and economic development in Ecuador.

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