Results 21 to 30 of about 61,584 (299)
pixelNeRF: Neural Radiance Fields from One or Few Images [PDF]
Computer Vision and Pattern Recognition, 2020 We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields [27] involves optimizing the representation to every ...
Alex Yu +3 more
semanticscholar +1 more source
Compact 3D Gaussian Representation for Radiance Field [PDF]
Computer Vision and Pattern Recognition, 2023 Neural Radiance Fields (NeRFs) have demonstrated re-markable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hin-ders the widespread adoption of NeRFs is the computational bottleneck due to the ...
J. Lee +4 more
semanticscholar +1 more source
PlenOctrees for Real-time Rendering of Neural Radiance Fields [PDF]
IEEE International Conference on Computer Vision, 2021 We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects.
Alex Yu +5 more
semanticscholar +1 more source
Remote Sensing, 2021 In April 2020, due to the coronavirus pandemic and the tourism decrease in Cracow (Poland), the Road Authority of the City of Cracow, followed by the authorities of several neighbouring municipalities, decided to turn off street lighting at night.
Tomasz Ściężor
doaj +1 more source
Atmosphere, 2023 The evolution of atmospheric and Earth surface physical properties over a period of 15 years (based on data from the longer period from 2004 to 2022) is analyzed through the radiance fluxes measured by the Meteosat second generation (MSG) satellite ...
José I. Prieto Fernández +1 more
doaj +1 more source
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo [PDF]
IEEE International Conference on Computer Vision, 2021 We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that consider per-scene optimization on densely captured images, we propose a ...
Anpei Chen +6 more
semanticscholar +1 more source
D-NeRF: Neural Radiance Fields for Dynamic Scenes [PDF]
Computer Vision and Pattern Recognition, 2020 Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images.
Albert Pumarola +3 more
semanticscholar +1 more source
NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads [PDF]
ACM Transactions on Graphics, 2023 We focus on reconstructing high-fidelity radiance fields of human heads, capturing their animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time steps.
Tobias Kirschstein +4 more
semanticscholar +1 more source
Robust Dynamic Radiance Fields [PDF]
Computer Vision and Pattern Recognition, 2023 Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms ...
Y. Liu +8 more
semanticscholar +1 more source
Point-NeRF: Point-based Neural Radiance Fields [PDF]
Computer Vision and Pattern Recognition, 2022 Volumetric neural rendering methods like NeRF [34] generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time.
Qiangeng Xu +6 more
semanticscholar +1 more source