Results 1 to 10 of about 171,197 (277)

Neural scene baking for permutation invariant transparency rendering with real-time global illumination

Computational Visual Media
Neural rendering provides a fundamentally new way to render photorealistic images. Similar to traditional light-baking methods, neural rendering utilizes neural networks to bake representations of scenes, materials, and lights into latent vectors learned
Ziyang Zhang, Edgar Simo-Serra
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SpiNeRF: direct-trained spiking neural networks for efficient neural radiance field rendering [PDF]

Frontiers in Neuroscience
Spiking neural networks (SNNs) have recently demonstrated significant progress across various computational tasks, due to their potential for energy efficiency.
Xingting Yao, Xingting Yao, Qinghao Hu, Fei Zhou, Tielong Liu, Tielong Liu, Zitao Mo, Zeyu Zhu, Zeyu Zhu, Zhengyang Zhuge, Jian Cheng, Jian Cheng   +11 more
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Comparing Neural Style Transfer and Gradient-Based Algorithms in Brushstroke Rendering Tasks

Mathematics, 2023
Non-photorealistic rendering (NPR) with explicit brushstroke representation is essential for both high-grade imitating of artistic paintings and generating commands for artistically skilled robots.
Artur Karimov, Ekaterina Kopets, Tatiana Shpilevaya, Evgenii Katser, Sergey Leonov, Denis Butusov   +5 more
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Trends and Techniques in 3D Reconstruction and Rendering: A Survey with Emphasis on Gaussian Splatting [PDF]

Sensors
Three-Dimensional Gaussian Splatting (3DGS), an important advancement in the field of computer graphics and 3D vision, has emerged to greatly accelerate the rendering process in novel views’ synthesis. Due to its ability to directly realize the real-time
Wenhe Chen, Zikai Li, Jingru Guo, Caixia Zheng, Siyi Tian   +4 more
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UniRender: Reconstructing 3D Surfaces from Aerial Images with a Unified Rendering Scheme

Remote Sensing, 2023
While recent advances in the field of neural rendering have shown impressive 3D reconstruction performance, it is still a challenge to accurately capture the appearance and geometry of a scene by using neural rendering, especially for remote sensing ...
Yiming Yan, Weikun Zhou, Nan Su, Chi Zhang   +3 more
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Neural Rendering-Based 3D Scene Style Transfer Method via Semantic Understanding Using a Single Style Image

Mathematics, 2023
In the rapidly emerging era of untact (“contact-free”) technologies, the requirement for three-dimensional (3D) virtual environments utilized in virtual reality (VR)/augmented reality (AR) and the metaverse has seen significant growth, owing to their ...
Jisun Park, Kyungeun Cho
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RepC-MVSNet: A Reparameterized Self-Supervised 3D Reconstruction Algorithm for Wheat 3D Reconstruction

Agronomy, 2023
The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are
Hui Liu, Cheng Xin, Mengzhen Lai, Hangfei He, Yongzhao Wang, Mantao Wang, Jun Li   +6 more
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Complex-Motion NeRF: Joint Reconstruction and Pose Optimization With Motion and Depth Priors

IEEE Access, 2023
We present Complex-Motion Neural Radiance Fields (CM-NeRF), which is a method that leverages motion and depth priors to optimize neural 3D scene representations and complex 6-DoF camera motions jointly.
Hyunjin Kim, Daekyeong Lee, Suyoung Kang, Pyojin Kim   +3 more
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Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation

Applied Sciences, 2021
Three-dimensional human pose and shape estimation is an important problem in the computer vision community, with numerous applications such as augmented reality, virtual reality, human computer interaction, and so on.
Junuk Cha, Muhammad Saqlain, Changhwa Lee, Seongyeong Lee, Seungeun Lee, Donguk Kim, Won-Hee Park, Seungryul Baek   +7 more
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PERF: Performant, Explicit Radiance Fields

Frontiers in Computer Science, 2022
We present a novel way of approaching image-based 3D reconstruction based on radiance fields. The problem of volumetric reconstruction is formulated as a non-linear least-squares problem and solved explicitly without the use of neural networks.
Sverker Rasmuson, Erik Sintorn, Ulf Assarsson   +2 more
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