PanoDreamer: Optimization-Based Single Image to 360 3D Scene With Diffusion
Avinash Paliwal, Xilong Zhou, Andrii Tsarov, Nima Khademi Kalantari
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Abstract
In this paper, we present PanoDreamer, a novel method for producing a coherent 360 3D scene from a single input image. Unlike existing methods that generate the scene sequentially, we frame the problem as single-image panorama and depth estimation. Once the coherent panoramic image and its corresponding depth are obtained, the scene can be reconstructed by inpainting the small occluded regions and projecting them into 3D space. Our key contribution is formulating single-image panorama and depth estimation as two optimization tasks and introducing alternating minimization strategies to effectively solve their objectives. We demonstrate that our approach outperforms existing techniques in single-image 360 3D scene reconstruction in terms of consistency and overall quality.