SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization

• URL: http://arxiv.org/abs/2407.09294v1
• Date: Fri, 12 Jul 2024 14:29:00 GMT
• Title: SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization
• Authors: Ashish Tiwari, Shanmuganathan Raman,
• Abstract summary: Shape from Polarization (SfP) is the problem popularly known as Shape from Polarization (SfP) We present a novel inverse rendering-based framework to estimate the 3D shape (per-pixel surface normals and depth) of objects and scenes from single-view polarization images.
• Score: 21.377923666134116
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a novel inverse rendering-based framework to estimate the 3D shape (per-pixel surface normals and depth) of objects and scenes from single-view polarization images, the problem popularly known as Shape from Polarization (SfP). The existing physics-based and learning-based methods for SfP perform under certain restrictions, i.e., (a) purely diffuse or purely specular reflections, which are seldom in the real surfaces, (b) availability of the ground truth surface normals for direct supervision that are hard to acquire and are limited by the scanner's resolution, and (c) known refractive index. To overcome these restrictions, we start by learning to separate the partially-polarized diffuse and specular reflection components, which we call reflectance cues, based on a modified polarization reflection model and then estimate shape under mixed polarization through an inverse-rendering based self-supervised deep learning framework called SS-SfP, guided by the polarization data and estimated reflectance cues. Furthermore, we also obtain the refractive index as a non-linear least squares solution. Through extensive quantitative and qualitative evaluation, we establish the efficacy of the proposed framework over simple single-object scenes from DeepSfP dataset and complex in-the-wild scenes from SPW dataset in an entirely self-supervised setting. To the best of our knowledge, this is the first learning-based approach to address SfP under mixed polarization in a completely self-supervised framework.

Related papers

• Video Frame Interpolation for Polarization via Swin-Transformer [9.10220649654041]
  Video Frame Interpolation (VFI) has been extensively explored and demonstrated, yet its application to polarization remains largely unexplored. This study proposes a multi-stage and multi-scale network called Swin-VFI based on the Swin-Transformer. Experimental results demonstrate our approach's superior reconstruction accuracy across all tasks.
  arXiv  Detail & Related papers  (2024-06-17T09:48:54Z)
• 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)
• Transparent Shape from a Single View Polarization Image [6.18278691318801]
  This paper presents a learning-based method for transparent surface estimation from a single view polarization image. Existing shape from polarization(SfP) methods have the difficulty in estimating transparent shape since the inherent transmission interference heavily reduces the reliability of physics-based prior.
  arXiv  Detail & Related papers  (2022-04-13T12:24:32Z)
• Shape from Polarization for Complex Scenes in the Wild [93.65746187211958]
  We present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image. We contribute the first real-world scene-level SfP dataset with paired input polarization images and ground-truth normal maps. Our trained model can be generalized to far-field outdoor scenes as the relationship between polarized light and surface normals is not affected by distance.
  arXiv  Detail & Related papers  (2021-12-21T17:30:23Z)
• Deep Polarization Imaging for 3D shape and SVBRDF Acquisition [7.86578678811226]
  We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints, we lift such restrictions by coupling polarization imaging with deep learning. We demonstrate our approach to achieve superior results compared to recent works employing deep learning in conjunction with flash illumination.
  arXiv  Detail & Related papers  (2021-05-06T17:58:43Z)
• Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces [103.08512487830669]
  This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. Existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. We propose an uncalibrated neural inverse rendering approach to this problem.
  arXiv  Detail & Related papers  (2020-12-12T10:33:08Z)
• Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization [99.96632216070718]
  Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution. The high-dimensional spatiality characteristic and complex geometrical structure of LF images make the problem more challenging than traditional single-image SR. We propose a novel learning-based LF framework, in which each view of an LF image is first individually super-resolved.
  arXiv  Detail & Related papers  (2020-04-05T14:39:57Z)
• Polarized Reflection Removal with Perfect Alignment in the Wild [66.48211204364142]
  We present a novel formulation to removing reflection from polarized images in the wild. We first identify the misalignment issues of existing reflection removal datasets. We build a new dataset with more than 100 types of glass in which obtained transmission images are perfectly aligned with input mixed images.
  arXiv  Detail & Related papers  (2020-03-28T13:29:31Z)

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.