UniSHARP: Universal Sharp Monocular View Synthesis

• URL: http://arxiv.org/abs/2606.07514v1
• Date: Fri, 05 Jun 2026 17:59:41 GMT
• Title: UniSHARP: Universal Sharp Monocular View Synthesis
• Abstract summary: We focus on extending SHARP, the popular view synthesis method, for universal monocular rendering across a continuum of camera systems.<n>To overcome the pinhole-specific assumptions of SHARP, our key idea is to align various images in a unified omnidirectional latent space.<n>We propose UniSHARP, which performs implicit alignment in both feature and Gaussian spaces.
• Score: 69.33495127820123
• License: http://creativecommons.org/licenses/by/4.0/
• 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/

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