| title | UniSHARP: Universal Sharp Monocular View Synthesis |
|---|---|
| authors | Meixi Song, Dizhe Zhang, Hao Ren, Ruiyang Zhang, Bo Du, Ming-Hsuan Yang, Lu Qi |
| date | 2026-06-05 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.07514 |
| upvotes | 15 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | In this work, we focus on extending SHARP, the popular photorealistic view synthesis method, for universal monocular rendering across a continuum of camera systems, from conventional perspective cameras to wide-field-of-view, fisheye and omnidirectional panoramic settings. To overcome the pinhole-specific assumptions of SHARP, our key idea is to align various images in a unified omnidirectional latent space. Thus, we propose UniSHARP, which performs implicit alignment in both feature and Gaussian spaces. Specifically, Gaussian primitives are arranged along rays and radial distances in a ray-based universal representation, while 2D semantic and 3D spatial features extracted from UniK3D-inspired encoders are jointly decoded to generate the complete Gaussian cloud. To comprehensively evaluate our method, we construct a benchmark covering diverse imaging systems across various scenes. The benchmark is further stratified by field of view (FoV) to enable fine-grained assessment of the universal monocular rendering task. Extensive experiments on the proposed benchmark demonstrate the effectiveness of UniSHARP, outperforming alternative methods by a large margin. The project page can be found at: https://insta360-research-team.github.io/Unisharp-website/ |
cite
@misc{song2026unisharp,
title={UniSHARP: Universal Sharp Monocular View Synthesis},
author={Meixi Song and Dizhe Zhang and Hao Ren and Ruiyang Zhang and Bo Du and Ming-Hsuan Yang and Lu Qi},
year={2026},
eprint={2606.07514},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.07514}
}