Benchmarking robustness in neural radiance fields
Abstract Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view
synthesis thanks to its ability to model 3D object geometries in a concise formulation …
synthesis thanks to its ability to model 3D object geometries in a concise formulation …
Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs
Abstract Neural Radiance Fields (NeRF) have emerged as a powerful representation for the
task of novel view synthesis due to their simplicity and state-of-the-art performance. Though …
task of novel view synthesis due to their simplicity and state-of-the-art performance. Though …
Vision transformer for nerf-based view synthesis from a single input image
Although neural radiance fields (NeRF) have shown impressive advances in novel view
synthesis, most methods require multiple input images of the same scene with accurate …
synthesis, most methods require multiple input images of the same scene with accurate …
Vip-nerf: Visibility prior for sparse input neural radiance fields
N Somraj, R Soundararajan - ACM SIGGRAPH 2023 Conference …, 2023 - dl.acm.org
Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by
encoding neural representations of a scene. However, NeRFs require hundreds of images …
encoding neural representations of a scene. However, NeRFs require hundreds of images …
Nerfren: Neural radiance fields with reflections
Abstract Neural Radiance Fields (NeRF) has achieved unprecedented view synthesis
quality using coordinate-based neural scene representations. However, NeRF's view …
quality using coordinate-based neural scene representations. However, NeRF's view …
X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360deg Insufficient RGB-D Views
H Zhu - Proceedings of the IEEE/CVF Winter Conference …, 2023 - openaccess.thecvf.com
Abstract Neural Radiance Fields (NeRFs), despite their outstanding performance on novel
view synthesis, often need dense input views. Many papers train one model for each scene …
view synthesis, often need dense input views. Many papers train one model for each scene …
Mixnerf: Modeling a ray with mixture density for novel view synthesis from sparse inputs
Abstract Neural Radiance Field (NeRF) has broken new ground in the novel view synthesis
due to its simple concept and state-of-the-art quality. However, it suffers from severe …
due to its simple concept and state-of-the-art quality. However, it suffers from severe …
pixelnerf: Neural radiance fields from one or few images
We propose pixelNeRF, a learning framework that predicts a continuous neural scene
representation conditioned on one or few input images. The existing approach for …
representation conditioned on one or few input images. The existing approach for …
NeRF--: Neural radiance fields without known camera parameters
Considering the problem of novel view synthesis (NVS) from only a set of 2D images, we
simplify the training process of Neural Radiance Field (NeRF) on forward-facing scenes by …
simplify the training process of Neural Radiance Field (NeRF) on forward-facing scenes by …
Structure-aware nerf without posed camera via epipolar constraint
S Chen, Y Zhang, Y Xu, B Zou - arXiv preprint arXiv:2210.00183, 2022 - arxiv.org
The neural radiance field (NeRF) for realistic novel view synthesis requires camera poses to
be pre-acquired by a structure-from-motion (SfM) approach. This two-stage strategy is not …
be pre-acquired by a structure-from-motion (SfM) approach. This two-stage strategy is not …