PERF (Panoramic Neural Radiance Fields) is a new framework that allows the transformation of single panorama images into 3D scenes that can be explored. It uses a collaborative RGBD inpainting method and a monocular depth estimator to generate novel appearances and 3D shapes. PERF achieves state-of-the-art results but relies on accurate depth estimation and stable diffusion. Future work will focus on improving these aspects.
Introducing PERF: The Panoramic NeRF Transforming Single Images into Explorable 3D Scenes
NeRF, which stands for Neural Radiance Fields, is a deep learning technique used for 3D scene reconstruction and view synthesis from 2D images. It requires multiple images or views of a scene to accurately construct a 3D representation. NeRF-W is an extension of NeRF that aims to make it more efficient, accurate, and applicable to various scenarios, including dynamic scenes and real-time applications.
However, if you only have a single image and want to incorporate 3D priors, the quality of the 3D reconstruction needs improvement. The present techniques have limitations in terms of field of view, which affects their scalability to real-world 360-degree panoramic scenarios with large sizes. That’s where PERF, or Panoramic Neural Radiance Field, comes in.
PERF is a 360-degree novel view synthesis framework that trains a panoramic neural radiance field from a single panorama. A panoramic image is created by stitching together multiple images to form a wide-angle representation of a scene. The PERF framework proposes a collaborative RGBD inpainting method to complete RGB images and depth maps of visible regions. It also includes a monocular depth estimator for depth completion to generate novel appearances and 3D shapes that are invisible from the input panorama.
The process of training a panoramic neural radiance field with PERF involves three steps: 1) obtaining single view NeRF training with depth supervision, 2) collaborating RGBD inpainting of regions of interest, and 3) using progressive inpainting-and-erasing generation. This approach tackles challenges such as lack of 3D information, large-size object occlusion, reconstruction and generation problems, and geometry conflicts between visible and invisible regions.
The researchers experimented with the Replica and PERF-in-the-wild datasets and demonstrated that PERF achieves a new state-of-the-art single-view panoramic neural radiance field. PERF has promising applications in panorama-to-3D, text-to-3D, and 3D scene stylization tasks.
While PERF significantly improves the performance of single-shot NeRF, it relies heavily on the accuracy of the depth estimator and the Stable Diffusion model. Future work will focus on improving the accuracy of these components.
To learn more about this research, you can check out the paper and GitHub repository. All credit goes to the researchers involved in this project.
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Evolve Your Company with AI: Introducing PERF
If you want to evolve your company with AI and stay competitive, consider using the Panoramic NeRF technique called PERF. This AI research introduces a practical solution for transforming single images into explorable 3D scenes.
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