3D Photography using Context-aware Layered Depth Inpainting

• URL: http://arxiv.org/abs/2004.04727v3
• Date: Wed, 10 Jun 2020 14:21:03 GMT
• Title: 3D Photography using Context-aware Layered Depth Inpainting
• Authors: Meng-Li Shih, Shih-Yang Su, Johannes Kopf, Jia-Bin Huang
• Abstract summary: We propose a method for converting a single RGB-D input image into a 3D photo. A learning-based inpainting model synthesizes new local color-and-depth content into the occluded region. The resulting 3D photos can be efficiently rendered with motion parallax.
• Score: 50.66235795163143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. The resulting 3D photos can be efficiently rendered with motion parallax using standard graphics engines. We validate the effectiveness of our method on a wide range of challenging everyday scenes and show fewer artifacts compared with the state of the arts.

Related papers

• ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis [49.28239918969784]
  We introduce a texture-consistent back view synthesis module that could transfer the reference image content to the back view. We also propose a visibility-aware patch consistency regularization for texture mapping and refinement combined with the synthesized back view texture.
  arXiv  Detail & Related papers  (2023-11-28T13:55:53Z)
• Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes [62.769186261245416]
  We present a novel inverse rendering framework for large urban scenes capable of jointly reconstructing the scene geometry, spatially-varying materials, and HDR lighting from a set of posed RGB images with optional depth. Specifically, we use a neural field to account for the primary rays, and use an explicit mesh (reconstructed from the underlying neural field) for modeling secondary rays that produce higher-order lighting effects such as cast shadows.
  arXiv  Detail & Related papers  (2023-04-06T17:51:54Z)
• TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering [54.35405028643051]
  We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps. We adopt the neural implicit surface reconstruction method, which allows for high-quality mesh.
  arXiv  Detail & Related papers  (2023-03-27T10:07:52Z)
• SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting [54.419266357283966]
  Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve compelling results. We present SLIDE, a modular and unified system for single image 3D photography.
  arXiv  Detail & Related papers  (2021-09-02T16:37:20Z)
• Realistic Image Synthesis with Configurable 3D Scene Layouts [59.872657806747576]
  We propose a novel approach to realistic-looking image synthesis based on a 3D scene layout. Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network. With the trained painting network, realistic-looking images for the input 3D scene can be rendered and manipulated.
  arXiv  Detail & Related papers  (2021-08-23T09:44:56Z)
• GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes [48.642181362172906]
  We present an algorithm that learns a coarse 3D representation of objects from unposed multi-view 2D mask supervision. In contrast to existing voxel-based methods for unposed object reconstruction, our approach learns to represent the generated shape and pose. We show results on synthetic datasets with realistic lighting, and demonstrate object insertion with interactive posing.
  arXiv  Detail & Related papers  (2021-06-24T17:47:58Z)
• GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis [43.4859484191223]
  We propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene. By introducing a multi-scale patch-based discriminator, we demonstrate synthesis of high-resolution images while training our model from unposed 2D images alone.
  arXiv  Detail & Related papers  (2020-07-05T20:37:39Z)

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.