Results 11 to 20 of about 17,338,731 (357)
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields [PDF]
Computer Vision and Pattern Recognition, 2021 Though neural radiance fields (NeRF) have demon-strated impressive view synthesis results on objects and small bounded regions of space, they struggle on “un-bounded” scenes, where the camera may point in any di-rection and content may exist at any ...
J. Barron +4 more
semanticscholar +1 more source
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields [PDF]
IEEE International Conference on Computer Vision, 2021 The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different ...
J. Barron +5 more
semanticscholar +1 more source
TensoRF: Tensorial Radiance Fields [PDF]
European Conference on Computer Vision, 2022 We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which represents a 3D voxel grid with per-voxel multi-channel features.
Anpei Chen +4 more
semanticscholar +1 more source
Plenoxels: Radiance Fields without Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2021 We introduce Plenoxels (plenoptic voxels), a systemfor photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics.
Alex Yu +5 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
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields [PDF]
Computer Vision and Pattern Recognition, 2016 We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image ...
Zhe Cao, T. Simon, S. Wei, Yaser Sheikh
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
BARF: Bundle-Adjusting Neural Radiance Fields [PDF]
IEEE International Conference on Computer Vision, 2021 Neural Radiance Fields (NeRF) [31] have recently gained a surge of interest within the computer vision community for its power to synthesize photorealistic novel views of real-world scenes.
Chen-Hsuan Lin +3 more
semanticscholar +1 more source
Nerfies: Deformable Neural Radiance Fields [PDF]
IEEE International Conference on Computer Vision, 2020 We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric ...
Keunhong Park +6 more
semanticscholar +1 more source
2D Gaussian Splatting for Geometrically Accurate Radiance Fields [PDF]
International Conference on Computer Graphics and Interactive Techniques3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed.
Binbin Huang +4 more
semanticscholar +1 more source