Results 21 to 30 of about 8,361,437 (204)
Communications of the ACM, 2020 We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D ...
Ben Mildenhall +5 more
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DReg-NeRF: Deep Registration for Neural Radiance Fields
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 Accepted at ICCV ...
Yu Chen, Gim Hee Lee
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Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
CoRR, 2023 We present a physics-enhanced implicit neural representation (INR) for ultrasound (US) imaging that learns tissue properties from overlapping US sweeps. Our proposed method leverages a ray-tracing-based neural rendering for novel view US synthesis. Recent publications demonstrated that INR models could encode a representation of a three-dimensional ...
Magdalena Wysocki +5 more
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Spike-NeRF: Neural Radiance Field Based On Spike Camera
2024 IEEE International Conference on Multimedia and Expo (ICME)As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking. Inspired by the success of the spike camera, we proposed Spike-NeRF, the first Neural Radiance Field derived from spike ...
Yijia Guo +6 more
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Point-NeRF: Point-based Neural Radiance Fields
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Accepted to CVPR 2022 (Oral)
Qiangeng Xu +6 more
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NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review [PDF]
, 2023 Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robotics,
He, Hongjie +5 more
core +1 more source
NeRF++: Analyzing and Improving Neural Radiance Fields
CoRR, 2020 Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes. NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and ...
Kai Zhang 0045 +3 more
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S-NeRF: Neural Radiance Fields for Street Views
CoRR, 2023 Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes.
Ziyang Xie +4 more
openaire +4 more sources
CG-NeRF: Conditional Generative Neural Radiance Fields
CoRR, 2021 While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics. In this paper, we propose a novel model, referred to as the conditional generative neural radiance fields (CG-NeRF), which can generate multi-view images ...
Kyungmin Jo +4 more
openaire +3 more sources
Eigengrasp-Conditioned Neural Radiance Fields
IEEE Access, 2023 In this study, we address the problem of learning a posture-controllable three-dimensional (3D) representation of articulated robotic hands. Neural radiance fields (NeRFs) have outperformed grid-like 3D representations on a novel view synthesis tasks ...
Hiroaki Aizawa, Itoshi Naramura
doaj +1 more source