The proceedings contain 50 papers. The topics discussed include: aperture localization based on vibro-stimuli generated from distance variation;training PointNet for human point cloud segmentation with 3d meshes;devel...
ISBN:
(纸本)9781510644267
The proceedings contain 50 papers. The topics discussed include: aperture localization based on vibro-stimuli generated from distance variation;training PointNet for human point cloud segmentation with 3d meshes;development of a 3dvisualization system for the cerebral aneurysm coil embolization;3dreconstruction of mirror-like surfaces by smart deflectometry;quality assessment of dynamic virtual relighting from RTI data: application to the inspection of engineering surfaces;tiny range image sensors using multiple laser lights for short distance measurement;development of image simulator for forward-looking sonar using 3d rendering;separation of compound actions with wrist and finger based on EMG;and latent space visualization of half face and full face by generative model.
The efficient representation, transmission, andreconstruction of three-dimensional (3d) contents are becoming increasingly important for sixth-generation (6G) networks that aim to merge virtual and physical worlds fo...
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The efficient representation, transmission, andreconstruction of three-dimensional (3d) contents are becoming increasingly important for sixth-generation (6G) networks that aim to merge virtual and physical worlds for offering immersive communication experiences. Neural radiance field (NeRF) and3d Gaussian splatting (3d-GS) have recently emerged as two promising 3d representation techniques based on radiance field rendering, which are able to provide photorealistic rendering results for complex scenes. Therefore, embracing NeRF and3d-GS in 6G networks is envisioned to be a prominent solution to support emerging 3d applications with enhanced quality of experience. This paper provides a comprehensive overview on the integration of NeRF and3d-GS in 6G. First, we review the basics of the radiance field rendering techniques, and highlight their applications and implementation challenges over wireless networks. Next, we consider the over-the-air training of NeRF and3d-GS models over wireless networks by presenting various learning techniques. We particularly focus on the federated learning design over a hierarchical device-edge-cloud architecture, which is suitable for exploiting distributeddata and computing resources over 6G networks to train large models representing large-scale scenes. Then, we consider the over-the-air rendering of NeRF and3d-GS models at wireless network edge. We present three practical rendering architectures, namely local, remote, and co-rendering, respectively, and provide model compression approaches to facilitate the transmission of radiance field models for rendering. We also present rendering acceleration approaches and joint computation and communication designs to enhance the rendering efficiency. In a case study, we propose a new semantic communication enabled3d content transmission design, in which the radiance field models are exploited as the semantic knowledge base to reduce the communication overhead for distributed inference.
Patient's understanding on forthcoming dental surgeries is required by patient-centered care and helps reduce anxiety. due to the complexity of dental surgeries and the patient-dentist expertise gap, conventional ...
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ISBN:
(纸本)9781450380171
Patient's understanding on forthcoming dental surgeries is required by patient-centered care and helps reduce anxiety. due to the complexity of dental surgeries and the patient-dentist expertise gap, conventional techniques of patient education are usually not effective for explaining surgical steps. In this paper, we present OralViewer-the first interactive application that enables dentist's demonstration of dental surgeries in 3d to promote patients' understanding. OralViewer takes a single 2d panoramic dental X-ray to reconstruct patient-specific 3d teeth structures, which are then assembled with registered gum and jaw bone models for complete oral cavity modeling. during the demonstration, OralViewer enables dentists to show surgery steps with virtualdental instruments that can animate effects on a 3d model in real-time. A technical evaluation shows that our deep learning model achieves a mean Intersection over Union (IoU) of 0.771 for 3d teeth reconstruction. A patient study with 12 participants shows OralViewer can improve patients' understanding of surgeries. A preliminary expert study with 3 board-certifieddentists further verifies the clinical validity of our system.
Modelling uncertainty of complex3d geological fields can require several sophisticated geostatistical methods. Such methods include process like methods that mimic the physics of deposition to create more realistic g...
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ISBN:
(纸本)9789462824263
Modelling uncertainty of complex3d geological fields can require several sophisticated geostatistical methods. Such methods include process like methods that mimic the physics of deposition to create more realistic geological models. Performing a full field history matching with such generative tools can be extremely challenging due to the large number of parameters that are used to generate each stochastic realization, the static data conditioning, and the complex and non-linear input-output relation. Ensemble data assimilation methods such as ESMdA (ensemble smoother with multiple data assimilation) are not directly applicable or can perform poorly due to the strong input parameter dependence and non-linearity. In this work we present some recent advances on the usage of GAN (Generative Adversarial Networks) to help solving the history matching problem when process like methods are used to generate multi-realizations to model the uncertainty of turbidite fields. GAN are deep learning generative models that are used in many AI applications. In history matching, using GAN to generate new geological models can help reducing drastically the input parameter space used to solve the inverse problem. In fact, to generate a new realization with a GAN we sample a random vector from a low dimensional independent Gaussian distribution (called the latent space). As a result, once the GAN is trained (using few thousands realizations generated with the process like methods), the inverse problem consists in having to invert only a few dozen independent parameters respect to a few millions dependent parameters in the original space. Usage of GAN for history matching of simple synthetic field cases has been discussed in previous works. However, to apply GAN on real field cases one needs to address several issues such as: - high number of cells of the input space (typical GAN architectures are build for 2d 128x128 images) - highly dependent input and non-linear input output relatio
With the widespread use of virtual reality applications, 3d scene generation has become a new challenging research frontier. 3d scenes have highly complex structures and need to ensure that the output is dense, cohere...
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2d image representations are in regular grids and can be processed efficiently, whereas 3d point clouds are unordered and scattered in 3d space. The information inside these two visual domains is well complementary, e...
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ISBN:
(纸本)9781665445092
2d image representations are in regular grids and can be processed efficiently, whereas 3d point clouds are unordered and scattered in 3d space. The information inside these two visual domains is well complementary, e.g., 2d images have fine-grained texture while 3d point clouds contain plentiful geometry information. However, most current visual recognition systems process them individually. In this paper, we present a bidirectional projection network (BPNet) for joint 2d and3d reasoning in an end-to-end manner. It contains 2d and3d sub-networks with symmetric architectures, that are connected by our proposed bidirectional projection module (BPM). Via the BPM, complementary 2d and3d information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition. Extensive quantitative and qualitative experimental evaluations show that joint reasoning over 2d and3d visual domains can benefit both 2d and3d scene understanding simultaneously. Our BPNet achieves top performance on the ScanNetV2 benchmark for both 2d and3d semantic segmentation.
densely captured real-world materials require effective compression for rendering, material generation andreconstruction. Neural networks with high compression rates and the ability to fit complex functions can encod...
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ISBN:
(数字)9798350362459
ISBN:
(纸本)9798350362466
densely captured real-world materials require effective compression for rendering, material generation andreconstruction. Neural networks with high compression rates and the ability to fit complex functions can encode each BRdF into the corresponding network. However, current works that take advantage of single implicit neural representations are incapable of effectively modeling the high-frequency details of the highlight region. In this paper, we propose an improved compact neural network representation of BRdF data based on the sinusoidal activation. The lightweight network and the periodic activation function improve the fidelity of the reproduction material appearance under the condition of a high compression rate. Furthermore, the method of building a unified model using neural networks can decode all materials from latent space. However, the deep structure of the network model increases memory consumption. To overcome this challenge, we propose a hypernetwork framework that compresses measured BRdFs to latent space and generates weights for the neural network-based representation of materials. The lightweight implicit representation of BRdF generated by training directly from original materials shows the characteristics of a low memory footprint and high-precision reproduction of appearance. Additionally, we apply the hypernetwork to reconstruct materials from a single image. Thanks to implicit representation of BRdF that can reproduce the appearance with high fidelity, the reflectance properties can be accurately recovered.
Hip fracture is the most common and serious fracture in the elderly. Because of the complicated types of acetabular fractures and the curved surface of acetabulum, the surgical treatment of two rows of acetabular frac...
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Radia is a 3d magnetostatics code that is widely used to model a range of magnets for particle accelerators, with particularly broad usage within the synchrotron light source community. Recently, Radia has been releas...
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In 3d, time-domain full-waveform inversion (FWI) is generally favored to solve the wave equation with explicit schemes. Furthermore, extended-space approaches such as wavefieldreconstruction inversion (WRI) or extend...
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