HAD: Hallucination-Aware Diffusion Priors for 3D Reconstruction
Diffusion priors have recently demonstrated strong capability in enhancing the quality of sparse-view 3D reconstruction by augmenting training views at novel viewpoints, but they inevitably introduce hallucinated content -- artifacts inconsistent with the input views -- into the final 3D model. To address this challenge, we propose Hallucination-Aware Diffusion prior (HAD), which estimates pixel-wise hallucination score maps for augmented images by leveraging multi-view reasoning capabilities from a feedforward novel view synthesis (NVS) network pre-trained on large-scale 3D data. These hallucination scores enable selective masking of unreliable pixels during the progressive 3D reconstruc...
| Authors | Xi Liu, Weiwei Sun, Zhou Ren et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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