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Computer Graphics Forum, 2022 AbstractSynthesizing photo‐realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input.
Ayush Tewari +16 more
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ACM Transactions on Graphics, 2019 The modern computer graphics pipeline can synthesize images at remarkable visual quality; however, it requires well-defined, high-quality 3D content as input. In this work, we explore the use of imperfect 3D content, for instance, obtained from photo-metric reconstructions with noisy and incomplete surface geometry, while still aiming to ...
Matthias Niessner, Justus Thies
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SpiNeRF: direct-trained spiking neural networks for efficient neural radiance field rendering
Frontiers in NeuroscienceSpiking neural networks (SNNs) have recently demonstrated significant progress across various computational tasks, due to their potential for energy efficiency.
Xingting Yao +11 more
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Neural Lumigraph Rendering [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Project website: http://www.computationalimaging.org/publications/nlr/
Petr Kellnhofer +5 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 +3 more
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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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CoRR, 2022 Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature.
Daniel Anderson +2 more
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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 +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 +3 more
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Neural Scene De-rendering [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 We study the problem of holistic scene understanding. We would like to obtain a compact, expressive, and interpretable representation of scenes that encodes information such as the number of objects and their categories, poses, positions, etc. Such a representation would allow us to reason about and even reconstruct or manipulate elements of the scene.
Wu, Jiajun +2 more
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