Towards Monocular Shape from Refraction

• URL: http://arxiv.org/abs/2305.19743v1
• Date: Wed, 31 May 2023 11:09:37 GMT
• Title: Towards Monocular Shape from Refraction
• Authors: Antonin Sulc, Imari Sato, Bastian Goldluecke, Tali Treibitz
• Abstract summary: We show that a simple energy function based on Snell's law enables the reconstruction of an arbitrary refractive surface geometry. We show that solving for an entire surface at once introduces implicit parameter-free spatial regularization.
• Score: 23.60349429048409
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Refraction is a common physical phenomenon and has long been researched in computer vision. Objects imaged through a refractive object appear distorted in the image as a function of the shape of the interface between the media. This hinders many computer vision applications, but can be utilized for obtaining the geometry of the refractive interface. Previous approaches for refractive surface recovery largely relied on various priors or additional information like multiple images of the analyzed surface. In contrast, we claim that a simple energy function based on Snell's law enables the reconstruction of an arbitrary refractive surface geometry using just a single image and known background texture and geometry. In the case of a single point, Snell's law has two degrees of freedom, therefore to estimate a surface depth, we need additional information. We show that solving for an entire surface at once introduces implicit parameter-free spatial regularization and yields convincing results when an intelligent initial guess is provided. We demonstrate our approach through simulations and real-world experiments, where the reconstruction shows encouraging results in the single-frame monocular setting.

Related papers

• NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images [62.752710734332894]
  NeRSP is a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. We derive photometric and geometric cues from the polarimetric image formation model and multiview azimuth consistency. We achieve the state-of-the-art surface reconstruction results with only 6 views as input.
  arXiv  Detail & Related papers  (2024-06-11T09:53:18Z)
• NeISF: Neural Incident Stokes Field for Geometry and Material Estimation [50.588983686271284]
  Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes, materials, or illuminations from a sequence of images captured under different viewpoints. We propose Neural Incident Stokes Fields (NeISF), a multi-view inverse framework that reduces ambiguities using polarization cues.
  arXiv  Detail & Related papers  (2023-11-22T06:28:30Z)
• NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation [36.09503501647977]
  We formulate the lighting of a static scene as one neural incident light field (NeILF) and one outgoing neural radiance field (NeRF) The proposed method is able to achieve state-of-the-art results in terms of geometry reconstruction quality, material estimation accuracy, and the fidelity of novel view rendering.
  arXiv  Detail & Related papers  (2023-03-30T04:59:48Z)
• NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects [28.62468618676557]
  We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects. With 2D images of the transparent object as input, our method is capable of high-quality novel view and relighting synthesis.
  arXiv  Detail & Related papers  (2023-03-21T15:50:08Z)
• Deformable Surface Reconstruction via Riemannian Metric Preservation [9.74575494970697]
  Estimating the pose of an object from a monocular image is an inverse problem fundamental in computer vision. This paper presents an approach to inferring continuous deformable surfaces from a sequence of images.
  arXiv  Detail & Related papers  (2022-12-22T10:45:08Z)
• SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery [54.64663713249079]
  SIDER is a novel photometric optimization method that recovers detailed facial geometry from a single image in an unsupervised manner. In contrast to prior work, SIDER does not rely on any dataset priors and does not require additional supervision from multiple views, lighting changes or ground truth 3D shape.
  arXiv  Detail & Related papers  (2021-08-11T22:34:53Z)
• Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance [86.05191217004415]
  Multi-view reconstruction of texture-less objects with unknown surface reflectance is a challenging task. This paper proposes a simple and robust solution to this problem based on a co-light scanner.
  arXiv  Detail & Related papers  (2021-05-25T01:28:54Z)
• Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction [40.696591594772876]
  We introduce a fixed viewpoint approach to dense surface reconstruction of transparent objects based on refraction of light. We present a setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid.
  arXiv  Detail & Related papers  (2021-05-20T19:01:12Z)
• Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs [93.72048616001064]
  Images captured under such condition suffer from a combination of geometric deformation and space varying blur. We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image.
  arXiv  Detail & Related papers  (2020-07-16T15:25:08Z)
• Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes [75.63464905190061]
  Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this problem. We propose a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera. Our experiments show successful recovery of high-quality 3D geometry for complex transparent shapes using as few as 5-12 natural images.
  arXiv  Detail & Related papers  (2020-04-22T23:51:30Z)

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.