360-Degree Panorama Generation from Few Unregistered NFoV Images

• URL: http://arxiv.org/abs/2308.14686v1
• Date: Mon, 28 Aug 2023 16:21:51 GMT
• Title: 360-Degree Panorama Generation from Few Unregistered NFoV Images
• Authors: Jionghao Wang, Ziyu Chen, Jun Ling, Rong Xie and Li Song
• Abstract summary: 360$circ$ panoramas are extensively utilized as environmental light sources in computer graphics. capturing a 360$circ$ $times$ 180$circ$ panorama poses challenges due to specialized and costly equipment. We propose a novel pipeline called PanoDiff, which efficiently generates complete 360$circ$ panoramas.
• Score: 16.05306624008911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 360$^\circ$ panoramas are extensively utilized as environmental light sources in computer graphics. However, capturing a 360$^\circ$ $\times$ 180$^\circ$ panorama poses challenges due to the necessity of specialized and costly equipment, and additional human resources. Prior studies develop various learning-based generative methods to synthesize panoramas from a single Narrow Field-of-View (NFoV) image, but they are limited in alterable input patterns, generation quality, and controllability. To address these issues, we propose a novel pipeline called PanoDiff, which efficiently generates complete 360$^\circ$ panoramas using one or more unregistered NFoV images captured from arbitrary angles. Our approach has two primary components to overcome the limitations. Firstly, a two-stage angle prediction module to handle various numbers of NFoV inputs. Secondly, a novel latent diffusion-based panorama generation model uses incomplete panorama and text prompts as control signals and utilizes several geometric augmentation schemes to ensure geometric properties in generated panoramas. Experiments show that PanoDiff achieves state-of-the-art panoramic generation quality and high controllability, making it suitable for applications such as content editing.

Related papers

• DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion [60.45000652592418]
  We propose a novel text-driven panoramic generation framework, DiffPano, to achieve scalable, consistent, and diverse panoramic scene generation. We show that DiffPano can generate consistent, diverse panoramic images with given unseen text descriptions and camera poses.
  arXiv  Detail & Related papers  (2024-10-31T17:57:02Z)
• VidPanos: Generative Panoramic Videos from Casual Panning Videos [73.77443496436749]
  Panoramic image stitching provides a unified, wide-angle view of a scene that extends beyond the camera's field of view. We present a method for synthesizing a panoramic video from a casually-captured panning video. Our system can create video panoramas for a range of in-the-wild scenes including people, vehicles, and flowing water.
  arXiv  Detail & Related papers  (2024-10-17T17:53:24Z)
• LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation [105.52153675890408]
  3D immersive scene generation is a challenging yet critical task in computer vision and graphics. LayerPano3D is a novel framework for full-view, explorable panoramic 3D scene generation from a single text prompt.
  arXiv  Detail & Related papers  (2024-08-23T17:50:23Z)
• Taming Stable Diffusion for Text to 360° Panorama Image Generation [74.69314801406763]
  We introduce a novel dual-branch diffusion model named PanFusion to generate a 360-degree image from a text prompt. We propose a unique cross-attention mechanism with projection awareness to minimize distortion during the collaborative denoising process.
  arXiv  Detail & Related papers  (2024-04-11T17:46:14Z)
• Scaled 360 layouts: Revisiting non-central panoramas [5.2178708158547025]
  We present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout.
  arXiv  Detail & Related papers  (2024-02-02T14:55:36Z)
• PERF: Panoramic Neural Radiance Field from a Single Panorama [109.31072618058043]
  PERF is a novel view synthesis framework that trains a panoramic neural radiance field from a single panorama. We propose a novel collaborative RGBD inpainting method and a progressive inpainting-and-erasing method to lift up a 360-degree 2D scene to a 3D scene. Our PERF can be widely used for real-world applications, such as panorama-to-3D, text-to-3D, and 3D scene stylization applications.
  arXiv  Detail & Related papers  (2023-10-25T17:59:01Z)
• PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas [54.4948540627471]
  We propose PanoGRF, Generalizable Spherical Radiance Fields for Wide-baseline Panoramas. Unlike generalizable radiance fields trained on perspective images, PanoGRF avoids the information loss from panorama-to-perspective conversion. Results on multiple panoramic datasets demonstrate that PanoGRF significantly outperforms state-of-the-art generalizable view synthesis methods.
  arXiv  Detail & Related papers  (2023-06-02T13:35:07Z)
• Moving in a 360 World: Synthesizing Panoramic Parallaxes from a Single Panorama [13.60790015417166]
  We present Omnidirectional Neural Radiance Fields ( OmniNeRF), the first method to the application of parallax-enabled novel panoramic view synthesis. We propose to augment the single RGB-D panorama by projecting back and forth between a 3D world and different 2D panoramic coordinates at different virtual camera positions. As a result, the proposed OmniNeRF achieves convincing renderings of novel panoramic views that exhibit the parallax effect.
  arXiv  Detail & Related papers  (2021-06-21T05:08:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.