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Ray Launching-Based Computation of Exact Paths with Noisy Dense Point Clouds

IEEE Transactions on Antennas and Propagation 2025年第5期73卷 3270-3283页

作者： Vaara, Niklas Sangi, Pekka Lopez, Miguel Bordallo Heikkila, Janne University of Oulu Faculty of Information Technology and Electrical Engineering Center for Machine Vision and Signal Analysis Finland

Point clouds have been a recent interest for ray tracing-based radio channel characterization, as sensors such as RGB-D cameras and laser scanners can be utilized to generate an accurate virtual copy of a physical environment. In this paper, a novel ray launching algorithm is presented, which operates directly on noisy point clouds acquired from sensor data. It produces coarse paths that are further refined to exact paths consisting of reflections and diffractions. A commercial ray tracing tool is utilized as the baseline for validating the simulated paths. A significant majority of the baseline paths is found. The robustness to noise is examined by artificially applying noise along the normal vector of each point. It is observed that the proposed method is capable of adapting to noise and finds similar paths compared to the baseline path trajectories with noisy point clouds. This is prevalent especially if the normal vectors of the points are estimated accurately. Lastly, a simulation is performed with a reconstructed point cloud and compared against channel measurements and the baseline paths. The resulting paths demonstrate similarity with the baseline path trajectories and exhibit an analogous pattern to the aggregated impulse response extracted from the measurements. Code available at https://***/nvaara/NimbusRT. © 1963-2012 IEEE.

关键词： Computer graphics equipment

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A Conic Transformation Approach for Solving the Perspective-Three-Point Problem

A Conic Transformation Approach for Solving the Perspective-...

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2025 IEEE/CVF Winter Conference on Applications of Computer vision, WACV 2025

作者： Wu, Haidong Bhayani, Snehal Heikkilä, Janne University of Oulu Center for Machine Vision and Signal Analysis Oulu Finland

ISBN: (纸本)9798331510831

We propose a conic transformation method to solve the Perspective-Three-Point (P3P) problem. In contrast to the current state-of-the-art solvers, which formulate the P3P problem by intersecting two conics and constructing a de-generate conic to find the intersection, our approach builds upon a new formulation based on a transformation that maps the two conics to a new coordinate system, where one of the conics becomes a standard parabola in a canonical form. This enables expressing one variable in terms of the other variable, and as a consequence, substantially simpli-fies the problem of finding the conic intersection. Moreover, the polynomial coefficients are fast to compute, and we only need to determine the real-valued intersection points, which avoids the requirement of using computationally expensive complex arithmetic. While the current state-of-the-art methods reduce the conic intersection problem to solving a univariate cubic equation, our approach, despite resulting in a quartic equation, is still faster thanks to this new simplified formulation. Extensive evaluations demonstrate that our method achieves higher speed while maintaining robustness and stability comparable to state-of-the-art methods. © 2025 IEEE.

关键词： Polynomials

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Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Learning Binary-Antithetical Information Bottleneck for Gene...

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2025 IEEE International Conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Yu, Hao Chen, Haoyu Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

ISBN: (纸本)9798350368741

We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: ***/CV-AC/BIB-FAS. © 2025 IEEE.

关键词： Binary-antithetical transition Domain generalization Face anti-spoofing Information bottleneck

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Demonstration of a Continuously Updated, Radio-Compatible Digital Twin for Robotic Integrated Sensing and Communications 5

Demonstration of a Continuously Updated, Radio-Compatible Di...

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5th IEEE International Symposium on Joint Communications and Sensing, JC and S 2025

作者： Andrei, Vlad C. Susarla, Praneeth Djuhera, Aladin Vaara, Niklas Mustaniemi, Janne Casado, Constantino Álvarez Li, Xinyang Monich, Ullrich J. Boche, Holger López, Miguel Bordallo Technical University of Munich Munich Germany 6G Flagship University of Oulu Center for Machine Vision and Signal Analysis Finland

ISBN: (纸本)9798331531652

Integrated sensing and communications (ISAC) is essential for future 6 G, bridging physical and digital worlds by enabling wireless systems to sense and respond to their environment. Digital twins (DTs) enhance ISAC by providing real-time, data-driven models for applications like localization and autonomous navigation. However, existing DT frameworks lack real-time modeling and multimodal sensing, limiting their use for high-resolution ISAC. This demo paper extends our recently proposed real-time DT framework for indoor ISACenabled robotics and introduces an LLM-driven speech interface for control and planning, a continuous 3D reconstruction scheme using RGB-D data, and a novel ray tracing approach for wireless channel modeling from point clouds. These innovations address key limitations, supporting advanced ISAC applications and immersive human-computer interaction. © 2025 IEEE.

关键词： Digital elevation model

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Exploring Facial Kinship Verification through Contactless Heart Activity analysis

Exploring Facial Kinship Verification through Contactless He...

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2025 IEEE International Conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Wu, Xiaoting Feng, Xiaoyi Casado, Constantino Álvarez Liu, Lili López, Miguel Bordallo Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Facial Kinship Verification (FKV) aims at automatically determining whether two subjects have a kinship relation based on human faces. It has potential applications in finding missing children and social media analysis. Traditional FKV faces challenges as it is vulnerable to spoof attacks and raises privacy issues. In this paper, we explore for the first time the FKV by analyzing cardiac activity through physiological signals, with a specific focus on remote Photoplethysmography (rPPG). rPPG signals are extracted from facial videos, resulting in a one-dimensional signal that measures the changes in visible light reflection emitted to and detected from the skin caused by the heartbeat. Specifically, in this paper, we employed a straightforward one-dimensional Convolutional Neural Network (1DCNN) with a 1DCNN-Attention module and kinship contrastive loss to learn the kin similarity from rPPGs. The network takes multiple rPPG signals extracted from various facial Regions of Interest (ROIs) as inputs. Additionally, the 1DCNN attention module is designed to learn and capture the discriminative kin features from feature embeddings. Finally, we demonstrate the feasibility of rPPG to detect kinship with the experiment evaluation on the UvANEMO Smile Database from different kin relations. © 2025 IEEE.

关键词： 1DCNN Biosignals Channel attention Kinship verification Remote Photoplethysmography

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RS+rPPG: Robust Strongly Self-Supervised Learning for rPPG

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Savic, Marko Zhao, Guoying University of Oulu Center for Machine Vision and Signal Analysis Oulu90014 Finland

Remote photoplethysmography (rPPG) uses RGB facial videos to measure cardiac signals. It holds promise for future applications in telemedicine, affective computing, liveness-based face anti-spoofing, driver monitoring, etc. Supervised deep learning methods have been leading in performance but are severely limited by data availability, as recording face videos with ground truth physiological signals is expensive. Recent self-supervised methods aim to solve the data issue but struggle to learn robust features from data in challenging scenarios. These scenarios are characterized by overwhelming environmental noise caused by head movements, illumination variations, and recording device changes. We propose RS+rPPG, a novel contrastive method that effectively leverages a large set of eleven rPPG priors, enabling strong self-supervision even with challenging data. RS+rPPG comprehensively exploits intra-data and interdata information present in videos via diverse augmentations and learning constraints. We extensively experimented on seven rPPG datasets and demonstrated that RS+rPPG can outperform state-of-the-art supervised methods without using any labels. Additionally, we demonstrate the high generalization capability, demographic fairness, and mixed-data stability of our method. © 2024 IEEE.

关键词： Self-supervised learning

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A Conic Transformation Approach for Solving the Perspective-Three-Point Problem

A Conic Transformation Approach for Solving the Perspective-...

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IEEE Workshop on Applications of Computer vision (WACV)

作者： Haidong Wu Snehal Bhayani Janne Heikkilä Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： Computer vision Robustness Polynomials Matrix converters Standards

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A Conic Transformation Approach for Solving the Perspective-Three-Point Problem

arXiv

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arXiv 2025年

作者： Wu, Haidong Bhayani, Snehal Heikkilä, Janne Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland

We propose a conic transformation method to solve the Perspective-Three-Point (P3P) problem. In contrast to the current state-of-the-art solvers, which formulate the P3P problem by intersecting two conics and constructing a degenerate conic to find the intersection, our approach builds upon a new formulation based on a transformation that maps the two conics to a new coordinate system, where one of the conics becomes a standard parabola in a canonical form. This enables expressing one variable in terms of the other variable, and as a consequence, substantially simplifies the problem of finding the conic intersection. Moreover, the polynomial coefficients are fast to compute, and we only need to determine the real-valued intersection points, which avoids the requirement of using computationally expensive complex arithmetic. While the current state-of-the-art methods reduce the conic intersection problem to solving a univariate cubic equation, our approach, despite resulting in a quartic equation, is still faster thanks to this new simplified formulation. Extensive evaluations demonstrate that our method achieves higher speed while maintaining robustness and stability comparable to state-of-the-art methods. © 2025, CC BY.

关键词： Equations of state

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Spatio-Temporal 3D Point Clouds from Wi-Fi-CSI Data via Transformer Networks

Spatio-Temporal 3D Point Clouds from Wi-Fi-CSI Data via Tran...

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Joint Communications & Sensing (JC&S), IEEE International Online Symposium on

作者： Tuomas Määttä Sasan Sharifipour Miguel Bordallo López Constantino Álvarez Casado Center for Machine Vision and Signal Analysis (CMVS) University of Oulu Finland

ISBN: (数字)9798331531652

ISBN: (纸本)9798331531669

Joint communication and sensing (JC&S) is emerging as a key component in 5G and 6G networks, enabling the dynamic adaptation to environmental changes and enhancing contextual awareness for optimized communication. By leveraging real-time environmental data, JC&S improves resource allocation, reduces latency, and enhancing power efficiency, while also supporting simulations and predictive modeling. This makes it a key technology for reactive systems and digital twins. These systems can respond to environmental events in real-time, offering transformative potential in sectors such as smart cities, healthcare, and Industry 5.0, where adaptive and multimodal interaction is critical to enhance real-time decision-making. In this work, we present a transformer-based architecture that processes temporal Channel State Information (CSI) data, specifically amplitude and phase, to generate 3D point clouds of indoor environments. The model utilizes multihead attention to capture complex spatio-temporal relationships in CSI data and is adaptable to different CSI configurations. We evaluate the architecture on the MM-Fi dataset, using two different protocols to capture human presence in indoor environments. The system demonstrates strong potential for accurate 3D reconstructions and effectively distinguishes between close and distant objects, advancing JC&S applications for spatial sensing in future wireless networks. The code is available at: https://***/Arritmic/csi2pointcloud.

关键词： Point cloud compression Adaptation models Three-dimensional displays Protocols Wireless networks Medical services Predictive models Transformers Data models Real-time systems

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Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Learning Binary-Antithetical Information Bottleneck for Gene...

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International Conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Hao Yu Haoyu Chen Guoying Zhao Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Training Representation learning Technological innovation Correlation Codes signal processing Data mining Speech processing Faces Optimization

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