AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360° Unbounded Scene Inpainting

• URL: http://arxiv.org/abs/2502.05176v1
• Date: Fri, 07 Feb 2025 18:59:55 GMT
• Title: AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360° Unbounded Scene Inpainting
• Authors: Chung-Ho Wu, Yang-Jung Chen, Ying-Huan Chen, Jie-Ying Lee, Bo-Hsu Ke, Chun-Wei Tuan Mu, Yi-Chuan Huang, Chin-Yang Lin, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu,
• Abstract summary: Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. We also introduce 360-USID, the first comprehensive dataset for 360deg unbounded scene inpainting with ground truth.
• Score: 15.177483700681377
• License:
• Abstract: Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360{\deg} unbounded scenes. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. Our approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. We also introduce 360-USID, the first comprehensive dataset for 360{\deg} unbounded scene inpainting with ground truth. Extensive experiments demonstrate that AuraFusion360 significantly outperforms existing methods, achieving superior perceptual quality while maintaining geometric accuracy across dramatic viewpoint changes. See our project page for video results and the dataset at https://kkennethwu.github.io/aurafusion360/.

Related papers

• DepthLab: From Partial to Complete [80.58276388743306]
  Missing values remain a common challenge for depth data across its wide range of applications. This work bridges this gap with DepthLab, a foundation depth inpainting model powered by image diffusion priors. Our approach proves its worth in various downstream tasks, including 3D scene inpainting, text-to-3D scene generation, sparse-view reconstruction with DUST3R, and LiDAR depth completion.
  arXiv  Detail & Related papers  (2024-12-24T04:16:38Z)
• Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation [6.832852988957967]
  We propose a new depth estimation framework that utilizes unlabeled 360-degree data effectively. Our approach uses state-of-the-art perspective depth estimation models as teacher models to generate pseudo labels. We tested our approach on benchmark datasets such as Matterport3D and Stanford2D3D, showing significant improvements in depth estimation accuracy.
  arXiv  Detail & Related papers  (2024-06-18T17:59:31Z)
• Sp2360: Sparse-view 360 Scene Reconstruction using Cascaded 2D Diffusion Priors [51.36238367193988]
  We tackle sparse-view reconstruction of a 360 3D scene using priors from latent diffusion models (LDM) We present SparseSplat360, a method that employs a cascade of in-painting and artifact removal models to fill in missing details and clean novel views. Our method generates entire 360 scenes from as few as 9 input views, with a high degree of foreground and background detail.
  arXiv  Detail & Related papers  (2024-05-26T11:01:39Z)
• DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting [56.101576795566324]
  We present a text-to-3D 360$circ$ scene generation pipeline. Our approach utilizes the generative power of a 2D diffusion model and prompt self-refinement. Our method offers a globally consistent 3D scene within a 360$circ$ perspective.
  arXiv  Detail & Related papers  (2024-04-10T10:46:59Z)
• NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes [59.15910989235392]
  We introduce NeO 360, Neural fields for sparse view synthesis of outdoor scenes. NeO 360 is a generalizable method that reconstructs 360deg scenes from a single or a few posed RGB images. Our representation combines the best of both voxel-based and bird's-eye-view (BEV) representations.
  arXiv  Detail & Related papers  (2023-08-24T17:59:50Z)
• NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360{\deg} Views [77.93662205673297]
  In this work, we study the challenging task of lifting a single image to a 3D object. We demonstrate the ability to generate a plausible 3D object with 360deg views that correspond well with a given reference image. We propose a novel framework, dubbed NeuralLift-360, that utilizes a depth-aware radiance representation.
  arXiv  Detail & Related papers  (2022-11-29T17:59:06Z)
• 360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network [35.03201732370496]
  Single-view depth estimation from omnidirectional images has gained popularity with its wide range of applications such as autonomous driving and scene reconstruction. In this work, we first establish a large-scale dataset with varied settings called Depth360 to tackle the training data problem. We then propose an end-to-end two-branch multi-task learning network, SegFuse, that mimics the human eye to effectively learn from the dataset.
  arXiv  Detail & Related papers  (2022-02-16T11:56:31Z)
• 360MonoDepth: High-Resolution 360{\deg} Monocular Depth Estimation [15.65828728205071]
  monocular depth estimation remains a challenge for 360deg data. Current CNN-based methods do not support such high resolutions due to limited GPU memory. We propose a flexible framework for monocular depth estimation from high-resolution 360deg images using tangent images.
  arXiv  Detail & Related papers  (2021-11-30T18:57:29Z)
• LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering [59.63979143021241]
  We formulate the task of 360 layout estimation as a problem of predicting depth on the horizon line of a panorama. We propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable. Our method achieves state-of-the-art performance on numerous 360 layout benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-01T15:48:41Z)
• A Fixation-based 360{\deg} Benchmark Dataset for Salient Object Detection [21.314578493964333]
  Fixation prediction (FP) in panoramic contents has been widely investigated along with the booming trend of virtual reality (VR) applications. salient object detection (SOD) has been seldom explored in 360deg images due to the lack of datasets representative of real scenes.
  arXiv  Detail & Related papers  (2020-01-22T11:16:39Z)

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.