Results 21 to 30 of about 24,246 (264)
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
NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior [PDF]
Computer Vision and Pattern Recognition, 2022 Training a Neural Radiance Field (NeRF) without precomputed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes.
Wen-Jing Bian +4 more
semanticscholar +1 more source
Decomposing NeRF for Editing via Feature Field Distillation [PDF]
Neural Information Processing Systems, 2022 Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations. However, editing a scene represented by a NeRF is challenging, as
Sosuke Kobayashi +2 more
semanticscholar +1 more source
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections [PDF]
Computer Vision and Pattern Recognition, 2020 We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multi-layer perceptron to model the ...
Ricardo Martin-Brualla +5 more
semanticscholar +1 more source
Recursive-NeRF: An Efficient and Dynamically Growing NeRF
IEEE Transactions on Visualization and Computer Graphics, 2023 View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attention due to their high quality imagery and scalability to high resolution. However, the heavy computation required by its volumetric approach prevents NeRF from being useful in
Guo-Wei Yang +5 more
openaire +4 more sources
Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields [PDF]
Computer Vision and Pattern Recognition, 2021 Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location.
Dor Verbin +5 more
semanticscholar +1 more source
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly- Throughs [PDF]
Computer Vision and Pattern Recognition, 2021 We use neural radiance fields (NeRFs) to build interac-tive 3D environments from large-scale visual captures spanning buildings or even multiple city blocks collected pri-marily from drones.
Haithem Turki +2 more
semanticscholar +1 more source
Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction [PDF]
IEEE International Conference on Computer Vision, 2023 3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging.
Hansheng Chen +6 more
semanticscholar +1 more source
Proceedings of the International Conference on Applied Innovations in IT, 2023 This paper addresses the problem of novel view synthesis using Neural Radiance Fields (NeRF) for scenes with dynamic illumination. NeRF training utilizes photometric consistency loss that is pixel-wise consistency between a set of scene images and ...
Olena Kolodiazhna +3 more
doaj +1 more source
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion [PDF]
International Conference on Machine Learning, 2023 Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input.
Jiatao Gu +6 more
semanticscholar +1 more source