PanoDiffusion: 360-degree Panorama Outpainting via Diffusion

• URL: http://arxiv.org/abs/2307.03177v6
• Date: Wed, 20 Mar 2024 04:43:27 GMT
• Title: PanoDiffusion: 360-degree Panorama Outpainting via Diffusion
• Authors: Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham,
• Abstract summary: We present our 360-degree indoor RGB-D panorama outpainting model using latent diffusion models (LDM), called PanoDiffusion. We introduce a new bi-modal latent diffusion structure that utilizes both RGB and depth panoramic data during training, which works surprisingly well to outpaint depth-free RGB images during inference. Results show that our PanoDiffusion not only significantly outperforms state-of-the-art methods on RGB-D panorama outpainting by producing diverse well-structured results for different types of masks, but can also synthesize high-quality depth panoramas to provide realistic 3D indoor models.
• Score: 29.10215003547589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating complete 360-degree panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available. Existing GAN-based approaches face some barriers to achieving higher quality output, and have poor generalization performance over different mask types. In this paper, we present our 360-degree indoor RGB-D panorama outpainting model using latent diffusion models (LDM), called PanoDiffusion. We introduce a new bi-modal latent diffusion structure that utilizes both RGB and depth panoramic data during training, which works surprisingly well to outpaint depth-free RGB images during inference. We further propose a novel technique of introducing progressive camera rotations during each diffusion denoising step, which leads to substantial improvement in achieving panorama wraparound consistency. Results show that our PanoDiffusion not only significantly outperforms state-of-the-art methods on RGB-D panorama outpainting by producing diverse well-structured results for different types of masks, but can also synthesize high-quality depth panoramas to provide realistic 3D indoor models.

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)
• Towards Robust Monocular Depth Estimation in Non-Lambertian Surfaces [12.241301077789235]
  We propose non-Lambertian surface regional guidance for monocular depth estimation. We employ the random tone-mapping augmentation during training to ensure the network can predict correct results for varying lighting inputs. Our method achieves accuracy improvements of 33.39% and 5.21% in zero-shot testing on the Booster and Mirror3D dataset.
  arXiv  Detail & Related papers  (2024-08-12T11:58:45Z)
• MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation [54.27399121779011]
  We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. We show that our approach can yield more accurate synthesis compared to recent state-of-the-art, including distillation-based 3D inference and prior multi-view generation methods.
  arXiv  Detail & Related papers  (2024-04-04T17:59:57Z)
• 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)
• BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning [26.24526760567159]
  We propose a novel bi-modal (RGB-D) panorama synthesis framework. We focus on indoor environments where the RGB-D panorama can provide a complete 3D model for many applications. Our method synthesizes high-quality indoor RGB-D panoramas and provides realistic 3D indoor models.
  arXiv  Detail & Related papers  (2021-12-12T08:20:01Z)
• Urban Radiance Fields [77.43604458481637]
  We perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings. Each of these three extensions provides significant performance improvements in experiments on Street View data.
  arXiv  Detail & Related papers  (2021-11-29T15:58:16Z)
• Deep Multi Depth Panoramas for View Synthesis [70.9125433400375]
  We present a novel scene representation - Multi Depth Panorama (MDP) - that consists of multiple RGBD$alpha$ panoramas. MDPs are more compact than previous 3D scene representations and enable high-quality, efficient new view rendering.
  arXiv  Detail & Related papers  (2020-08-04T20:29:15Z)

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.