A benchmark dataset for objective quality assessment of view synthesis for neural radiance field (NeRF)Figshare
Neural Radiance Fields (NeRF) are revolutionizing diverse fields such as autonomous driving, education, and virtual reality (VR). As their applications expand, the ability to accurately evaluate the quality of NeRF-generated content becomes essential. Currently, there are only a few datasets for NeR...
Saved in:
| Main Authors: | Chibuike Onuoha, Shihao Luo, Jean Flaherty, Truong Thu Huong, Pham Ngoc Nam, Truong Cong Thang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925002161 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Unmanned Aerial Vehicle-Neural Radiance Field (UAV-NeRF): Learning Multiview Drone Three-Dimensional Reconstruction with Neural Radiance Field
by: Li Li, et al.
Published: (2024-11-01) -
NeRF View Synthesis: Subjective Quality Assessment and Objective Metrics Evaluation
by: Pedro Martin, et al.
Published: (2025-01-01) -
YOLO-NeRFSLAM: underwater object detection for the visual NeRF-SLAM
by: Zhe Wang, et al.
Published: (2025-06-01) -
Comparison of NeRF- and SfM-Based Methods for Point Cloud Reconstruction for Small-Sized Archaeological Artifacts
by: Miguel Ángel Maté-González, et al.
Published: (2025-07-01) -
Density uncertainty quantification with NeRF-Ensembles: Impact of data and scene constraints
by: Miriam Jäger, et al.
Published: (2025-03-01)