depth estimation and object instance recognition capabilities in 3d space are critical for autonomous navigation, localization, mapping, and robotic object manipulation. RGB-d images and LidAR (Light detection and Ran...
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The field of three-dimensional reconstruction plays a pivotal role across diverse domains such as computer graphics, virtual reality, robotics, archaeology, and medical imaging. This paper presents a novel deep learni...
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ISBN:
(数字)9798350371598
ISBN:
(纸本)9798350371604
The field of three-dimensional reconstruction plays a pivotal role across diverse domains such as computer graphics, virtual reality, robotics, archaeology, and medical imaging. This paper presents a novel deep learning framework for three-dimensional object reconstruction utilizing 3d point clouds, specifically focusing on a Multi-Task Regressor within a Convolutional Neural Network (CNN) architecture. Acknowledging the challenges associated with applying convolutions directly to point clouds, we adopt an innovative approach by building upon the PointNet architecture. Our adaptation integrates a Multi-Task Regressor to achieve highly accurate reconstructions of Supershapes from 3d point clouds. Our methodology involves employing PointNet to extract features from the input 3d point cloud. Subsequently, these features are inputted into a Multi-Task Regressor, which simultaneously predicts multiple outputs. In the context of Supershape reconstruction, the Multi-Task Regressor predicts the essential parameters needed to faithfully recreate the intricate shape. Unlike traditional single-output regressors, our approach considers and predicts multiple facets of the reconstruction process concurrently, enhancing the overall efficiency and accuracy of the model. The results of our experiments surpassed expectations, demonstrating notable improvements in precision and predictive cost. This research not only contributes to the advancement of three-dimensional reconstruction techniques but also highlights the efficacy of employing a Multi-Task Regressor for handling diverse regression objectives within the context of complex shape reconstruction from 3d point clouds.
Lithoseismic deduces lithologies from seismic data. In turbiditic context, it implies a 3d mapping of channelized systems. These structures may be extremely complexdue to their formation but also the tectonic forces ...
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This study investigates the application of a low-cost Kinect sensor for 3dreconstruction inside. The project uses structured light technologies and the Kinect depth sensor to collect and rebuild interior surroundings...
This study investigates the application of a low-cost Kinect sensor for 3dreconstruction inside. The project uses structured light technologies and the Kinect depth sensor to collect and rebuild interior surroundings in three dimensions. The primary goal is to provide realistic 3d representations of interior locations for a variety of purposes. Using the Kinect sensor and specialized software, interior scenes are scanned and then recreated. The outcomes indicate how well the Kinect-based method works for producing accurate indoor 3dreconstructions, and also suggest possible uses for it in a number of industries, including robotics, virtual reality, and architectural visualization. This work advances the use of Kinect sensors for indoor 3dreconstruction, providing new opportunities for improved spatial comprehension andvirtual environment engagement.
Brain tumor volume quantification is not possible with magnetic resonance imaging (MRI) non-invasive imaging systems. Usually, the brain MR imaging modality is based on four modalities T1, T1ce (contrast-enhanced), T2...
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This work presents a system which allows image data and extracted features from a real-world location to be captured and modelled in a virtual Reality (VR) environment combined with Augmented Situatedvisualizations (...
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ISBN:
(纸本)9781665440578
This work presents a system which allows image data and extracted features from a real-world location to be captured and modelled in a virtual Reality (VR) environment combined with Augmented Situatedvisualizations (ASV) overlaid and registered in a virtual environment. Combining these technologies with techniques from data Science and Artificial Intelligence (AI)(such as image analysis and3dreconstruction) allows the creation of a setting where remote locations can be modelled and interacted with from any-where in the world. This Enhanced Photorealistic Immersive (EPI) system is highly adaptable to a wide range of use cases and users as it can be utilized to model and interact with any environment which can be captured as image data (such as training for operation in hazardous environments, accessibility solutions for exploration of historical/tourism locations and collaborative learning environments). A use case example focused on a structural examination of railway tunnels along with a pilot study is presented, which can demonstrate the usefulness of the EPI system.
Easy and fast digitalization of real objects is especially useful when considering augmented reality (AR) andvirtual reality (VR), as reconstructed objects allow a better interaction between the real andvirtual worl...
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We present a novel, effective method for global indoor scene reconstruction problems by geometric topology. Based on point cloud pairwise registration methods (e.g ICP) or IMU, we focus on the problem of accumulated e...
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We present a novel, effective method for global indoor scene reconstruction problems by geometric topology. Based on point cloud pairwise registration methods (e.g ICP) or IMU, we focus on the problem of accumulated error for the composition of transformations along any loops. The major technical contribution of this paper is a linear method for the graph optimation, using only solving a Poisson equation. We demonstrate the consistency of our method from Hodge-Helmhotz decomposition theorem and experiments on multiple RGBddatasets of indoor scene. The experimental results also demonstrate that our global registration method runs quickly and provides accurate reconstructions.
The quality of 3d blade model reconstructiondetermines the accuracy of blade manufacturing. However, scanning equipment must gather data from multiple viewpoints to obtain a complete model due to the blade's comp...
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ISBN:
(数字)9798350387780
ISBN:
(纸本)9798350387797
The quality of 3d blade model reconstructiondetermines the accuracy of blade manufacturing. However, scanning equipment must gather data from multiple viewpoints to obtain a complete model due to the blade's complex shape anddiscontinuous surface. The choice of sensor views has a notable impact on the reconstruction task, commonly referred to as the nest-best-view (NBV) planning problem. The preferred approach generally involves either surface-based or volumetric methods. A deep reinforcement learning approach is proposed in this article to address the problem of determining the most suitable viewpoint. In addition, a virtual simulation model for 3d measuring system of turbine blades is built to acquire 3d measurement data and sensor pose. Based on this foundation, a deep reinforcement learning model is constructed and evaluated on two different shapes of blades datasets. The experiment's results showcase that the algorithm surpasses the baseline in the number of viewpoints.
The proceedings contain 176 papers. The special focus in this conference is on Multimedia Modeling. The topics include: GWUNet: A UNet with Gated Attention and Improved Wavelet Transform for Thyroid Nodules Segmentati...
ISBN:
(纸本)9789819620708
The proceedings contain 176 papers. The special focus in this conference is on Multimedia Modeling. The topics include: GWUNet: A UNet with Gated Attention and Improved Wavelet Transform for Thyroid Nodules Segmentation;HCV: Lightweight Hybrid CNN-Vision Transformer for Visual Object Tracking;hierArtEx: Hierarchical Representations and Art Experts Supporting the Retrieval of Museums in the Metaverse;hybrid Scalable Video Coding with Neural Compression and Enhancement for Streaming Media;Hyper-NeuS: Hypernetworks for Neural SdF Implicit Surface reconstruction by Volume Rendering;Image-Generation AI Model Retrieval by Contrastive Learning-Based Style distance Calculation;Improving Singing Voice Transcription Generalization with AI Generated Accompaniments;infrared Small Target detection with Feature Refinement and Context Enhancement;innovative Lifelog visualization and Exploration in virtual Reality – A Comparative Study;integrating S1 &S2 Framework for Enhanced Semantic Match in Person Re-identification;intra-class Compact Facial Expression Recognition Based on Amplitude Phase Separation;joint decision Network with Modality-Specific anddual Interactive Features for Fake News detection;kiite World: Socializing Map-Based Music Exploration Through Playlist Sharing and Synchronized Listening;kuzushijidiffuser: Japanese Kuzushiji Font Generation with Fontdiffuser;LIESA: Low-Light Image Enhancement with Semantic Awareness;lightweight dual Grouped Large-Kernel Convolutions for Salient Object detection Network;lightweight Motion-Aware Video Super-Resolution for Compressed Videos;LITA: LMM-Guided Image-Text Alignment for Art Assessment;LLMs-Based Augmentation for domain Adaptation in Long-Tailed Fooddatasets;making Strides Security in Multimodal Fake News detection Models: A Comprehensive Analysis of Adversarial Attacks;mambaTalk: Speech-driven 3d Facial Animation with Mamba;MC-YOLO: Multi-scale Transmission Line defect Target Recognition Network;MdT-Net: A Mask decoder Tu
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