Papers Sensor Modeling
“Sensor Modeling” 태그가 달린 논문 25편 · 필터 해제
Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation
Sensor simulation is pivotal for scalable validation of autonomous driving systems, yet existing Neural Radiance Fields (NeRF) based methods face applicability and efficiency challenges in industrial workflows. This pape…
Autonomous DrivingData AugmentationNeRFSensor ModelingOceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework
Underwater simulators offer support for building robust underwater perception solutions. Significant work has recently been done to develop new simulators and to advance the performance of existing underwater simulators.…
GPUSensor ModelingSynthetic Data GenerationTransferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies
Data-driven soft sensors help in process operations by providing real-time estimates of otherwise hard- to-measure process quantities, e.g., viscosities or product concentrations. Currently, soft sensors need to be devel…
Graph Neural NetworkSensor ModelingTransfer LearningA Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval
Data-driven soft sensors are crucial in predicting key performance indicators in industrial systems. However, current methods predominantly rely on the supervised learning paradigms of parameter updating, which inherentl…
In-Context LearningSensor ModelingUncertainty QuantificationVariable SelectionLiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene Reconstruction
Photorealistic 3D scene reconstruction plays an important role in autonomous driving, enabling the generation of novel data from existing datasets to simulate safety-critical scenarios and expand training data without ad…
3D Scene ReconstructionAutonomous DrivingSensor ModelingCapacitive Touch Sensor Modeling With a Physics-informed Neural Network and Maxwell's Equations
Maxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch sensors, which are wid…
Sensor ModelingLSE-NeRF: Learning Sensor Modeling Errors for Deblured Neural Radiance Fields with RGB-Event Stereo
We present a method for reconstructing a clear Neural Radiance Field (NeRF) even with fast camera motions. To address blur artifacts, we leverage both (blurry) RGB images and event camera data captured in a binocular con…
NeRFSensor ModelingAccelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models
In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, nume…
Sensor ModelingNeuRAD: Neural Rendering for Autonomous Driving
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training…
Autonomous DrivingData AugmentationNeural RenderingNovel View Synthesis+1Unsupervised Cross-Domain Soft Sensor Modelling via Deep Physics-Inspired Particle Flow Bayes
Data-driven soft sensors are essential for achieving accurate perception through reliable state inference. However, developing representative soft sensor models is challenged by issues such as missing labels, domain adap…
Domain AdaptationMissing LabelsSensor ModelingTime Series+1Solving PDEs with Unmeasurable Source Terms Using Coupled Physics-Informed Neural Network with Recurrent Prediction for Soft Sensors
Partial differential equations (PDEs) are a model candidate for soft sensors in industrial processes with spatiotemporal dependence. Although physics-informed neural networks (PINNs) are a promising machine learning meth…
Sensor ModelingThermal Image Processing via Physics-Inspired Deep Networks
We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by …
DenoisingSensor ModelingSuper-ResolutionAuto-encoder based Model for High-dimensional Imbalanced Industrial Data
With the proliferation of IoT devices, the distributed control systems are now capturing and processing more sensors at higher frequency than ever before. These new data, due to their volume and novelty, cannot be effect…
Representation LearningSensor ModelingVocal Bursts Intensity PredictionSystematic Categorization of Influencing Factors on Radar-Based Perception to Facilitate Complex Real-World Data Evaluation
For the assessment of machine perception for automated driving it is important to understand the influence of certain environment factors on the sensors used. Especially when investigating large amounts of real-world dat…
Sensor ModelingLearning to Drop Points for LiDAR Scan Synthesis
3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due to hardware limitations, weather conditio…
Point Cloud GenerationSensor ModelingA Review of Testing Object-Based Environment Perception for Safe Automated Driving
Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. We focus on testing f…
BenchmarkingSensor ModelingAsynchronous Multi-View SLAM
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchrono…
Sensor ModelingAI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors
The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports…
Feature EngineeringFeature ImportanceFPS GamesSensor Modeling+3Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
Current research in eSports lacks the tools for proper game practising and performance analytics. The majority of prior work relied only on in-game data for advising the players on how to perform better. However, in-game…
BIG-bench Machine LearningInterpretable Machine LearningMultimodal Deep LearningPerson Re-Identification+6Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Proper training and analytics in eSports require accurately collected and annotated data. Most eSports research focuses exclusively on in-game data analysis, and there is a lack of prior work involving eSports athletes' …
Feature ImportancePerson Re-IdentificationPhysiological ComputingReal-Time Strategy Games+4