PanoDreamer: Optimization-Based Single Image to 360 3D Scene With Diffusion (2412.04827v2)

Abstract: In this paper, we present PanoDreamer, a novel method for producing a coherent 360{\deg} 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{\deg} 3D scene reconstruction in terms of consistency and overall quality.

• The paper introduces PanoDreamer, a method that redefines panorama synthesis as an optimization problem with alternating minimization.
• It utilizes pre-trained inpainting diffusion models to generate a complete 360° panorama, overcoming drawbacks of sequential detail addition.
• Empirical results using metrics like CLIP-IQA+ and Q-Align demonstrate significant improvements in visual coherence and depth consistency.

PanoDreamer: The Advancement of 3D Panorama Synthesis from a Single Image

The paper PanoDreamer: 3D Panorama Synthesis from a Single Image investigates the novel task of synthesizing a coherent 360° 3D scene from a single input image, addressing limitations in existing state-of-the-art methodologies like LucidDreamer and WonderJourney. These existing methods exhibit imperfections, such as visible seams, due to their sequential detail addition approach. In contrast, PanoDreamer seeks to eliminate such inconsistencies by reformulating the panorama generation as an optimization problem with alternating minimization strategies.

The authors introduce a procedural framework that diverges from traditional methods by first developing a full 360° panorama using pre-trained inpainting diffusion models. This method integrates a more sophisticated use of diffusion models, similar to the approach proposed by MultiDiffusion, and reframes the single-image panorama and depth estimation tasks into optimization problems. The authors apply alternating minimization strategies to both problems, aiming to achieve a seamless panoramic image and depth consistency.

In terms of numerical achievements, the paper provides empirical evidence that PanoDreamer outperforms existing techniques in generating consistent 3D scenes from a single image. The approach demonstrates improvements in both visual coherence and quality metrics, surpassing the performance of related works in several consistency and style adherence dimensions. The assessment leverages metrics like CLIP-IQA+, Q-Align, and others to quantify the advancements PanoDreamer offers in synthesizing spherical panoramas.

Furthermore, this paper pushes the boundaries of computer vision and photorealistic rendering, revealing future implications for VR/AR, gaming, and immersive media. Specifically, by addressing the issues of visibility consistency and scene coherence from single images, this method offers a reliable basis for developing more sophisticated interactive and immersive environments.

The future prospects of this research suggest enhancements in monocular depth estimation tailored for large-scale panoramic images and integration with more complex 3D scene representations beyond Gaussian splatting. Such developments could facilitate a new wave of applications in graphical simulations and real-time rendering systems.

In conclusion, the PanoDreamer framework marks a significant stride toward improving the generation of 360° 3D panoramic scenes from single images. The methods and results presented in this work underscore its potential influence in advancing single-image 3D synthesis and suggest broader possibilities for future research explorations in AI-driven 3D scene reconstruction.