Results 21 to 30 of about 4,408 (223)

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, Gernot Riegler, Noah Snavely, Vladlen Koltun   +3 more
openaire   +2 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, Gyumin Shim, Sanghun Jung, Soyoung Yang, Jaegul Choo   +4 more
openaire   +2 more sources

SeaThru-NeRF: Neural Radiance Fields in Scattering Media

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Research on neural radiance fields (NeRFs) for novel view generation is exploding with new models and extensions. However, a question that remains unanswered is what happens in underwater or foggy scenes where the medium strongly influences the appearance of objects. Thus far, NeRF and its variants have ignored these cases.
Deborah Levy, Amit Peleg, Naama Pearl, Dan Rosenbaum, Derya Akkaynak, Simon Korman, Tali Treibitz   +6 more
openaire   +2 more sources

Ev-NeRF: Event Based Neural Radiance Field

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023
Accepted to WACV ...
Inwoo Hwang, Junho Kim, Young Min Kim 0001   +2 more
openaire   +2 more sources

Event-Based Camera Tracker by ∇_tNeRF

IEEE Access, 2023
When a camera travels across a 3D world, only a fraction of pixel value changes; an event-based camera observes the change as sparse events. How can we utilize sparse events for efficient recovery of the camera pose?
Mana Masuda, Yusuke Sekikawa, Hideo Saito   +2 more
doaj   +1 more source

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Training a Neural Radiance Field (NeRF) without pre-computed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement.
Bian, W, Wang, Z, Li, K, Bian, J-W, Prisacariu, VA   +4 more
openaire   +2 more sources

NAS-NeRF: Generative Neural Architecture Search for Neural Radiance Fields

CoRR, 2023
Neural radiance fields (NeRFs) enable high-quality novel view synthesis, but their high computational complexity limits deployability. While existing neural-based solutions strive for efficiency, they use one-size-fits-all architectures regardless of scene complexity.
Saeejith Nair, Yuhao Chen 0001, Mohammad Javad Shafiee, Alexander Wong   +3 more
openaire   +2 more sources

Assessment of 3D Model for Photogrammetric Purposes Using AI Tools Based on NeRF Algorithm

Heritage, 2023
The aim of the paper is to analyse the performance of the Neural Radiance Field (NeRF) algorithm, implemented in Instant-NGP software, for photogrammetric purposes.
Massimiliano Pepe, Vincenzo Saverio Alfio, Domenica Costantino   +2 more
doaj   +1 more source

Nation Scale NeRF Reconstruction [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Neural Radiance Field (NeRF) rendering methodologies provide 3D reconstructions which better represent features and characteristics common to built heritage, which are otherwise poorly represented by traditional structure from motion reconstruction and ...
A. Mai, S. McAvoy, S. McAvoy, J. Klingspon, D. Forcellini, F. Kuester   +5 more
doaj   +1 more source

BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Neural Radiance Fields (NeRF) have received considerable attention recently, due to its impressive capability in photo-realistic 3D reconstruction and novel view synthesis, given a set of posed camera images. Earlier work usually assumes the input images are of good quality. However, image degradation (e.g.
Peng Wang 0141, Lingzhe Zhao, Ruijie Ma, Peidong Liu 0001   +3 more
openaire   +2 more sources