Image Gradient-Aided Photometric Stereo Network

• URL: http://arxiv.org/abs/2412.11650v1
• Date: Mon, 16 Dec 2024 10:50:52 GMT
• Title: Image Gradient-Aided Photometric Stereo Network
• Authors: Kaixuan Wang, Lin Qi, Shiyu Qin, Kai Luo, Yakun Ju, Xia Li, Junyu Dong,
• Abstract summary: Photometric stereo endeavors to ascertain surface normals using shading clues from photometric images under various illuminations. Recent deep learning-based PS methods often overlook the complexity of object surfaces. We propose the Image Gradient-Aided Photometric Stereo Network (IGA-PSN), a dual-branch framework extracting features from both photometric images and their gradients.
• Score: 37.71540892622098
• License:
• Abstract: Photometric stereo (PS) endeavors to ascertain surface normals using shading clues from photometric images under various illuminations. Recent deep learning-based PS methods often overlook the complexity of object surfaces. These neural network models, which exclusively rely on photometric images for training, often produce blurred results in high-frequency regions characterized by local discontinuities, such as wrinkles and edges with significant gradient changes. To address this, we propose the Image Gradient-Aided Photometric Stereo Network (IGA-PSN), a dual-branch framework extracting features from both photometric images and their gradients. Furthermore, we incorporate an hourglass regression network along with supervision to regularize normal regression. Experiments on DiLiGenT benchmarks show that IGA-PSN outperforms previous methods in surface normal estimation, achieving a mean angular error of 6.46 while preserving textures and geometric shapes in complex regions.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• 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)
• A Neural Height-Map Approach for the Binocular Photometric Stereo Problem [36.404880059833324]
  binocular photometric stereo (PS) framework has same acquisition speed as single view PS, however significantly improves the quality of the estimated geometry. Our method achieves the state-of-the-art performance on the DiLiGenT-MV dataset adapted to binocular stereo setup as well as a new binocular photometric stereo dataset - LUCES-ST.
  arXiv  Detail & Related papers  (2023-11-10T09:45:53Z)
• Deep Learning Methods for Calibrated Photometric Stereo and Beyond [86.57469194387264]
  Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues. Deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces.
  arXiv  Detail & Related papers  (2022-12-16T11:27:44Z)
• PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanism [4.822598110892846]
  Existing deep calibrated photometric stereo networks aggregate observations under different lights based on pre-defined operations such as linear projection and max pooling. To tackle this issue, this paper presents a deep sparse calibrated photometric stereo network named it PS-Transformer which leverages the learnable self-attention mechanism to properly capture the complex inter-image interactions.
  arXiv  Detail & Related papers  (2022-11-21T11:58:25Z)
• 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)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• 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)
• Deep Photometric Stereo for Non-Lambertian Surfaces [89.05501463107673]
  We introduce a fully convolutional deep network for calibrated photometric stereo, which we call PS-FCN. PS-FCN learns the mapping from reflectance observations to surface normal, and is able to handle surfaces with general and unknown isotropic reflectance. To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images.
  arXiv  Detail & Related papers  (2020-07-26T15:20:53Z)

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.