MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset

• URL: http://arxiv.org/abs/2211.14118v2
• Date: Wed, 4 Oct 2023 09:29:07 GMT
• Title: MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset
• Authors: Cl\'ement Hardy, Yvain Qu\'eau, David Tschumperl\'e
• Abstract summary: Photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object. We propose a multi-scale architecture for PS which, combined with a new dataset, yields state-of-the-art results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object, thanks to a set of photographs taken under different lighting directions. In this paper, we propose a multi-scale architecture for PS which, combined with a new dataset, yields state-of-the-art results. Our proposed architecture is flexible: it permits to consider a variable number of images as well as variable image size without loss of performance. In addition, we define a set of constraints to allow the generation of a relevant synthetic dataset to train convolutional neural networks for the PS problem. Our proposed dataset is much larger than pre-existing ones, and contains many objects with challenging materials having anisotropic reflectance (e.g. metals, glass). We show on publicly available benchmarks that the combination of both these contributions drastically improves the accuracy of the estimated normal field, in comparison with previous state-of-the-art methods.

Related papers

• Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
  Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields. LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation. It can generate versatile label maps by interacting with language at novel viewpoints.
  arXiv  Detail & Related papers  (2024-10-24T17:54:42Z)
• RMAFF-PSN: A Residual Multi-Scale Attention Feature Fusion Photometric Stereo Network [37.759675702107586]
  Predicting accurate maps of objects from two-dimensional images in regions of complex structure spatial material variations is challenging. We propose a method of calibrated feature information from different resolution stages and scales of the image. This approach preserves more physical information, such as texture and geometry of the object in complex regions.
  arXiv  Detail & Related papers  (2024-04-11T14:05:37Z)
• FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic Data [27.53648027412686]
  We seek to develop a method for few-view reconstruction, for the case of the human foot. To solve this task, we must extract rich geometric cues from RGB images, before carefully fusing them into a final 3D object. We show that our normal predictor outperforms all off-the-shelf equivalents significantly on real images.
  arXiv  Detail & Related papers  (2023-10-27T17:11:07Z)
• Multi-View Photometric Stereo Revisited [100.97116470055273]
  Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D acquisition of an object from images. We present a simple, practical approach to MVPS, which works well for isotropic as well as other object material types such as anisotropic and glossy. The proposed approach shows state-of-the-art results when tested extensively on several benchmark datasets.
  arXiv  Detail & Related papers  (2022-10-14T09:46:15Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
  We present an efficient method for joint optimization of materials and lighting from multi-view image observations. We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
  arXiv  Detail & Related papers  (2021-11-24T13:58:20Z)
• Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
  We present a modern solution to the multi-view photometric stereo problem (MVPS) We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
  arXiv  Detail & Related papers  (2021-10-11T20:20:03Z)
• ResDepth: A Deep Prior For 3D Reconstruction From High-resolution Satellite Images [28.975837416508142]
  We introduce ResDepth, a convolutional neural network that learns such an expressive geometric prior from example data. In a series of experiments, we find that the proposed method consistently improves stereo DSMs both quantitatively and qualitatively. We show that the prior encoded in the network weights captures meaningful geometric characteristics of urban design.
  arXiv  Detail & Related papers  (2021-06-15T12:51:28Z)
• LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo [30.31403197697561]
  We introduce LUCES, the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of a varying of materials. A device counting 52 LEDs has been designed to lit each object positioned 10 to 30 centimeters away from the camera. We evaluate the performance of the latest near-field Photometric Stereo algorithms on the proposed dataset.
  arXiv  Detail & Related papers  (2021-04-27T12:30:42Z)
• Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
  We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object. We first estimate per-view depth maps using a deep multi-view stereo network. These depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture.
  arXiv  Detail & Related papers  (2020-03-27T21:28:54Z)

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.