Results 41 to 50 of about 2,209 (160)
Novel View Synthesis of Defocused Blur Scenes Based on Neural Radiance Fields
IEEE AccessIn recent years, neural radiance fields have been widely used in the field of computer graphics due to their excellent reconstruction quality. However, the shooting process in the wild environment is often affected by various internal and external ...
Zhaoji Lin, Yuxin Zheng, Li Yao
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Rugularizing generalizable neural radiance field with limited-view images
Complex & Intelligent SystemsWe present a novel learning model with attention and prior guidance for view synthesis. In contrast to previous works that focus on optimizing for specific scenes with densely captured views, our model explores a generic deep neural framework to ...
Wei Sun +4 more
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Embracing Radiance Field Rendering in 6G: Over-the-Air Training and Inference With 3-D Contents
IEEE Open Journal of the Communications SocietyThe efficient representation, transmission, and reconstruction 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 ...
Guanlin Wu +3 more
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Neural Radiance Fields-Comprehensive Survey
, 2023 Neural Radiance Fields (NeRF) is a machine learning model that can generate high-resolution, photorealistic 3D models of scenes or objects from a set of 2D images. It does this by learning a continuous 3D function that maps positions in 3D space to the radiance (intensity and color) of the light that would be observed at that position in the scene.
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Neural-radiance-fields-based holography [Invited]
Applied OpticsThis study presents, to the best of our knowledge, a novel approach for generating holograms based on the neural radiance fields (NeRF) technique. Generating real-world three-dimensional (3D) data is difficult in hologram computation. NeRF is a state-of-the-art technique for 3D light-field reconstruction from 2D images based on volume rendering.
Minsung Kang +4 more
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Expansive Supervision for Neural Radiance Fields
2025 IEEE International Conference on Multimedia and Expo (ICME)Neural Radiance Field (NeRF) has achieved remarkable success in creating immersive media representations through its exceptional reconstruction capabilities. However, the computational demands of dense forward passes and volume rendering during training continue to challenge its real-world applications. In this paper, we introduce Expansive Supervision
Zhang, Weixiang +5 more
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NeRF2: Neural Radio-Frequency Radiance Fields
Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, 2023 Although Maxwell discovered the physical laws of electromagnetic waves 160 years ago, how to precisely model the propagation of an RF signal in an electrically large and complex environment remains a long-standing problem. The difficulty is in the complex interactions between the RF signal and the obstacles (e.g., reflection, diffraction, etc ...
Xiaopeng Zhao +3 more
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Applied SciencesIn the field of multi-view satellite photogrammetry, the neural radiance field (NeRF) method has received widespread attention due to its ability to provide continuous scene representation and realistic rendering effects.
Xin Zhou +5 more
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Remote Sensing, 2021 Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation.
Mingshan Duan +6 more
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Novel View Synthesis and Dataset Augmentation for Hyperspectral Data Using NeRF
IEEE AccessHyperspectral data for the 3D domain is relatively difficult to acquire. Existing hyperspectral datasets are unsuitable for 3D research, suffer from issues of severe data scarcity, and a lack of multi-perspective images of the same object, etc.
Runchuan Ma +3 more
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