AS-NeRF: Learning Auxiliary Sampling for Generalizable Novel View Synthesis from Sparse Views
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2024 IEEE International Conference on Multimedia and Expo (ICME), 2024, 00, pp. 1-6
- Issue Date:
- 2024-09-30
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1758948.pdf | Published version | 2.73 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
We tackle the problem of novel view synthesis NVS which aims to generate realistic images at novel views Unlike existing works that require either costly per scene optimization or relatively dense views we propose AS NeRF a neural rendering based approach that can achieve generalizable NVS from only sparse views Considering the inherent spatial continuity of images we design a novel sparse attention based auxiliary sampling module ASM Given a 3D point on the ray the ASM adaptively attends to a sparse set of view specific 2D auxiliary locations around the 3D point s original projection pixels and dynamically computes the attention weights in a cross attention manner This enables our model to effectively exploit the local correlation among neighboring pixels obtaining the enhanced features with more powerful representation Extensive experiments show that our method outperforms the state of the art on both real and synthetic data
Please use this identifier to cite or link to this item: